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Xing Xie

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73 papers
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73

AAAI Conference 2026 Conference Paper

IROTE: Human-like Traits Elicitation of Large Language Model via In-Context Self-Reflective Optimization

  • Yuzhuo Bai
  • Shitong Duan
  • Muhua Huang
  • Jing Yao
  • Zhenghao Liu
  • Peng Zhang
  • Tun Lu
  • Xiaoyuan Yi

Trained on various human-authored corpora, Large Language Models (LLMs) have demonstrated a certain capability of reflecting specific human-like traits (e.g., personality or values) by prompting, benefiting applications like personalized LLMs and social simulations. However, existing methods suffer from the superficial elicitation problem: LLMs can only be steered to mimic shallow and unstable stylistic patterns, failing to embody the desired traits precisely and consistently across diverse tasks like humans. To address this challenge, we propose IROTE, a novel in-context method for stable and transferable trait elicitation. Drawing on psychological theories suggesting that traits are formed through identity-related reflection, our method automatically generates and optimizes a textual self-reflection within prompts, which comprises self-perceived experience, to stimulate LLMs' trait-driven behavior. The optimization is performed by iteratively maximizing an information-theoretic objective that enhances the connections between LLMs' behavior and the target trait, while reducing noisy redundancy in reflection without any fine-tuning, leading to evocative and compact trait reflection. Extensive experiments across three human trait systems manifest that one single IROTE-generated self-reflection can induce LLMs' stable impersonation of the target trait across diverse downstream tasks beyond simple questionnaire answering, consistently outperforming existing strong baselines.

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AAAI Conference 2026 Conference Paper

MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions

  • Yanxu Zhu
  • Shitong Duan
  • Xiangxu Zhang
  • Jitao Sang
  • Peng Zhang
  • Tun Lu
  • Xiao Zhou
  • Jing Yao

Recently Multimodal Large Language Models (MLLMs) have achieved considerable advancements in vision-language tasks, yet produce potentially harmful or untrustworthy content. Despite substantial work investigating the trustworthiness of language models, MMLMs' capability to act honestly, especially when faced with visually unanswerable questions, remains largely underexplored. This work presents the first systematic assessment of honesty behaviors across various MLLMs. We ground honesty in models' response behaviors to unanswerable visual questions, define four representative types of such questions, and construct MoHoBench, a large-scale MMLM honest benchmark, consisting of 12k+ visual question samples, whose quality is guaranteed by multi-stage filtering and human verification. Using MoHoBench, we benchmarked the honesty of 28 popular MMLMs and conducted a comprehensive analysis. Our findings show that: (1) most models fail to appropriately refuse to answer when necessary, and (2) MMLMs' honesty is not solely a language modeling issue, but is deeply influenced by visual information, necessitating the development of dedicated methods for multimodal honesty alignment. Therefore, we implemented initial alignment methods using supervised and preference learning to improve honesty behavior, providing a foundation for future work on trustworthy MLLMs.

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AAAI Conference 2026 Conference Paper

Unleashing the Potential of Large Language Models for Text-to-Image Generation Through Autoregressive Representation Alignment

  • Xing Xie
  • Jiawei Liu
  • Ziyue Lin
  • Huijie Fan
  • Zhi Han
  • Yandong Tang
  • Liangqiong Qu

We present Autoregressive Representation Alignment (ARRA), a new training framework that unlocks global-coherent text-to-image generation in autoregressive LLMs without architectural modifications. Different from prior works that require complex architectural redesigns, ARRA aligns LLM's hidden states with visual representations from external visual foundational models via a global visual alignment loss and a hybrid token,. This token enforces dual constraints: local next-token prediction and global semantic distillation, enabling LLMs to implicitly learn spatial and contextual coherence while retaining their original autoregressive paradigm. Extensive experiments validate ARRA's plug-and-play versatility. When training T2I LLMs from scratch, ARRA reduces FID by 16.6% (ImageNet), 12.0% (LAION-COCO) for autoregressive LLMs like LlamaGen, without modifying original architecture and inference mechanism. For training from text-generation-only LLMs, ARRA reduces FID by 25.5% (MIMIC-CXR), 8.8% (DeepEyeNet) for advanced LLMs like Chameleon. For domain adaptation, ARRA aligns general-purpose LLMs with specialized models (e.g., BioMedCLIP), achieving an 18.6% FID reduction over direct fine-tuning on medical imaging (MIMIC-CXR). These results demonstrate that training objective redesign, rather than architectural modifications, can resolve cross-modal global coherence challenges. ARRA offers a complementary paradigm for advancing autoregressive models.

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IROS Conference 2025 Conference Paper

A Bio-inspired Robotic Electric Ray Design of Multimodal Locomotion with Grasping Function

  • Yuyang Mo
  • Xing Xie
  • Zicun Hong
  • Huiping Zhuang
  • Yong Zhong

In nature, fish locomotion is primarily classified into the BCF (body and caudal fin) propulsion mode and the MPF (median and paired fin) propulsion mode. This paper presents a bio-inspired robotic electric ray that integrates a BCF-mode caudal fin with MPF-mode pectoral fins. The caudal fin consists of a set of wire-driven, multi-joint active segments coupled with a soft, compliant segment, while each symmetrical pectoral fin incorporates two sets of wire-driven joints and a soft fin structure. The undulatory motion of the MPF-mode pectoral fins enables the robotic ray to execute maneuvers such as forward swimming, backward swimming, and in-place turning, whereas the BCF-mode caudal fin enhances linear swimming and turning capabilities. Experimental results demonstrate that MPF-mode swimming achieves a maximum speed of 0. 190 m/s (0. 358 BL/s), while the cooperative propulsion of MPF and BCF modes enables speeds of up to 0. 352 m/s (0. 664 BL/s). Notably, the robotic electric ray's large pectoral fins can function as grippers, allowing it to grasp and transport objects using caudal fin propulsion, thereby facilitating object manipulation tasks.

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TIST Journal 2025 Journal Article

Aspect-Enhanced Explainable Recommendation with Multi-modal Contrastive Learning

  • Hao Liao
  • Shuo Wang
  • Hao Cheng
  • Wei Zhang
  • Jiwei Zhang
  • Mingyang Zhou
  • Kezhong Lu
  • Rui Mao

Explainable recommender systems ( ERS ) aim to enhance users’ trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the recommendations, fostering a sense of confidence and reliability in the system’s outputs. Generally, the explanations are presented in a familiar and intuitive way, which is in the form of natural language, thus enhancing their accessibility to users. Recently, there has been an increasing focus on leveraging reviews as a valuable source of rich information in both modeling user-item preferences and generating textual interpretations, which can be performed simultaneously in a multi-task framework. Despite the progress made in these review-based recommendation systems, the integration of implicit feedback derived from user-item interactions and user-written text reviews has yet to be fully explored. To fill this gap, we propose a model named SERMON (A s pect-enhanced E xplainable R ecommendation with M ulti-modal C o ntrast Lear n ing). Our model explores the application of multimodal contrastive learning to facilitate reciprocal learning across two modalities, thereby enhancing the modeling of user preferences. Moreover, our model incorporates the aspect information extracted from the review, which provides two significant enhancements to our tasks. Firstly, the quality of the generated explanations is improved by incorporating the aspect characteristics into the explanations generated by a pre-trained model with controlled textual generation ability. Secondly, the commonly used user-item interactions are transformed into user-item-aspect interactions, which we refer to as interaction triple, resulting in a more nuanced representation of user preference. To validate the effectiveness of our model, we conduct extensive experiments on three real-world datasets. The experimental results show that our model outperforms state-of-the-art baselines, with a 2.0% improvement in prediction accuracy and a substantial 24.5% enhancement in explanation quality for the TripAdvisor dataset.

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NeurIPS Conference 2025 Conference Paper

Counterfactual Reasoning for Steerable Pluralistic Value Alignment of Large Language Models

  • Hanze Guo
  • Jing Yao
  • Xiao Zhou
  • Xiaoyuan Yi
  • Xing Xie

As large language models (LLMs) become increasingly integrated into applications serving users across diverse cultures, communities, and demographics, it is critical to align LLMs with pluralistic human values beyond average principles (e. g. , HHH). In psychological and social value theories such as Schwartz’s Value Theory, pluralistic values are represented by multiple value dimensions paired with various priorities. However, existing methods encounter two challenges when aligning with such fine-grained value objectives: 1) they often treat multiple values as independent and equally important, ignoring their interdependence and relative priorities (value complexity); 2) they struggle to precisely control nuanced value priorities, especially those underrepresented ones (value steerability). To handle these challenges, we propose COUPLE, a COUnterfactual reasoning framework for PLuralistic valuE alignment. It introduces a structural causal model (SCM) to feature complex interdependency and prioritization among features, as well as the causal relationship between high-level value dimensions and behaviors. Moreover, it applies counterfactual reasoning to generate outputs aligned with any desired value objectives. Benefitting from explicit causal modeling, COUPLE also provides better interpretability. We evaluate COUPLE on two datasets with different value systems and demonstrate that COUPLE advances other baselines across diverse types of value objectives. Our code is available at https: //github. com/microsoft/COUPLE.

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NeurIPS Conference 2025 Conference Paper

Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study

  • Zhengyu Hu
  • Jianxun Lian
  • Zheyuan Xiao
  • Seraphina Zhang
  • Tianfu Wang
  • Nicholas Jing Yuan
  • Xing Xie
  • Hui Xiong

Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning, yet their learning ability, which is crucial for adapting to dynamic environments and acquiring new knowledge, remains underexplored. In this work, we address this gap by introducing a framework inspired by cognitive psychology and education. Specifically, we decompose general learning ability into three distinct, complementary dimensions: Learning from Instructor (acquiring knowledge via explicit guidance), Learning from Concept (internalizing abstract structures and generalizing to new contexts), and Learning from Experience (adapting through accumulated exploration and feedback). We conduct a comprehensive empirical study across the three learning dimensions and identify several insightful findings, such as (i) interaction improves learning; (ii) conceptual understanding is scale-emergent and benefits larger models; and (iii) LLMs are effective few-shot learners but not many-shot learners. Based on our framework and empirical findings, we introduce a benchmark that provides a unified and realistic evaluation of LLMs' general learning abilities across three learning cognition dimensions. It enables diagnostic insights and supports evaluation and development of more adaptive and human-like models.

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TIST Journal 2024 Journal Article

A Survey on Evaluation of Large Language Models

  • Yupeng Chang
  • Xu Wang
  • Jindong Wang
  • Yuan Wu
  • Linyi Yang
  • Kaijie Zhu
  • Hao Chen
  • Xiaoyuan Yi

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the ‘where’ and ‘how’ questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey

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AAAI Conference 2024 Conference Paper

Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations

  • Lei Li
  • Jianxun Lian
  • Xiao Zhou
  • Xing Xie

Retrieval models aim at selecting a small set of item candidates which match the preference of a given user. They play a vital role in large-scale recommender systems since subsequent models such as rankers highly depend on the quality of item candidates. However, most existing retrieval models employ a single-round inference paradigm, which may not adequately capture the dynamic nature of user preferences and stuck in one area in the item space. In this paper, we propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems that iteratively refines user representations to better capture potential candidates in the full item space. Ada-Retrieval comprises two key modules: the item representation adapter and the user representation adapter, designed to inject context information into items' and users' representations. The framework maintains a model-agnostic design, allowing seamless integration with various backbone models such as RNNs or Transformers. We perform experiments on three widely used public datasets, incorporating five powerful sequential recommenders as backbone models. Our results demonstrate that Ada-Retrieval significantly enhances the performance of various base models, with consistent improvements observed across different datasets. Our code and data are publicly available at: https://github.com/ll0ruc/Ada-Retrieval.

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NeurIPS Conference 2024 Conference Paper

CLAVE: An Adaptive Framework for Evaluating Values of LLM Generated Responses

  • Jing Yao
  • Xiaoyuan Yi
  • Xing Xie

The rapid progress in Large Language Models (LLMs) poses potential risks such as generating unethical content. Assessing the values embedded in LLMs' generated responses can help expose their misalignment, but this relies on reference-free value evaluators, e. g. fine-tuned LLMs or closed-source models like GPT-4. Nevertheless, two key challenges emerge in open-ended value evaluation: the evaluator should adapt to changing human value definitions with minimal annotation, against their own bias (adaptability); and remain robust across varying value expressions and scenarios (generalizability). To handle these challenges, we introduce CLAVE, a novel framework that integrates two complementary LLMs: a large model to extract high-level value concepts from diverse responses, leveraging its extensive knowledge and generalizability, and a small model fine-tuned on these concepts to adapt to human value annotations. This dual-model framework enables adaptation to any value system using <100 human-labeled samples per value type. We also present ValEval, a comprehensive dataset comprising 13k+ (text, value, label) tuples across diverse domains, covering three major value systems. We benchmark the performance of 15+ popular LLM evaluators and fully analyze their strengths and weaknesses. Our findings reveal that CLAVE combining a large prompt-based model and a small fine-tuned one serves as an optimal balance in value evaluation.

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NeurIPS Conference 2024 Conference Paper

CultureLLM: Incorporating Cultural Differences into Large Language Models

  • Cheng Li
  • Mengzhuo Chen
  • Jindong Wang
  • Sunayana Sitaram
  • Xing Xie

Large language models (LLMs) have been observed to exhibit bias towards certain cultures due to the predominance of training data obtained from English corpora. Considering that multilingual cultural data is often expensive to procure, existing methodologies address this challenge through prompt engineering or culture-specific pre-training. However, these strategies may neglect the knowledge deficiency of low-resource cultures and necessitate substantial computing resources. In this paper, we propose CultureLLM, a cost-effective solution to integrate cultural differences into LLMs. CultureLLM employs the World Value Survey (WVS) as seed data and generates semantically equivalent training data through the proposed semantic data augmentation. Utilizing only $50$ seed samples from WVS with augmented data, we fine-tune culture-specific LLMs as well as a unified model (CultureLLM-One) for $9$ cultures, encompassing both rich and low-resource languages. Extensive experiments conducted on $60$ culture-related datasets reveal that CultureLLM significantly surpasses various counterparts such as GPT-3. 5 (by $8. 1$\%) and Gemini Pro (by $9. 5$\%), demonstrating performance comparable to or exceeding that of GPT-4. Our human study indicates that the generated samples maintain semantic equivalence to the original samples, offering an effective solution for LLMs augmentation. Code is released at https: //github. com/Scarelette/CultureLLM.

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NeurIPS Conference 2024 Conference Paper

CulturePark: Boosting Cross-cultural Understanding in Large Language Models

  • Cheng Li
  • Damien Teney
  • Linyi Yang
  • Qingsong Wen
  • Xing Xie
  • Jindong Wang

Cultural bias is pervasive in many large language models (LLMs), largely due to the deficiency of data representative of different cultures. Typically, cultural datasets and benchmarks are constructed either by extracting subsets of existing datasets or by aggregating from platforms such as Wikipedia and social media. However, these approaches are highly dependent on real-world data and human annotations, making them costly and difficult to scale. Inspired by cognitive theories on social communication, this paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection. CulturePark simulates cross-cultural human communication with LLM-based agents playing roles in different cultures. It generates high-quality cross-cultural dialogues encapsulating human beliefs, norms, and customs. Using CulturePark, we generated 41, 000 cultural samples to fine-tune eight culture-specific LLMs. We evaluated these models across three downstream tasks: content moderation, cultural alignment, and cultural education. Results show that for content moderation, our GPT-3. 5-based models either match or outperform GPT-4 on $41$ datasets. Regarding cultural alignment, our models surpass GPT-4 on Hofstede's VSM 13 framework. Furthermore, for cultural education of human participants, our models demonstrate superior outcomes in both learning efficacy and user experience compared to GPT-4. CulturePark proves an important step in addressing cultural bias and advancing the democratization of AI, highlighting the critical role of culturally inclusive data in model training. Code is released at https: //github. com/Scarelette/CulturePark.

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NeurIPS Conference 2024 Conference Paper

ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models

  • Jio Oh
  • Soyeon Kim
  • Junseok Seo
  • Jindong Wang
  • Ruochen Xu
  • Xing Xie
  • Steven E. Whang

Large language models (LLMs) have achieved unprecedented performances in various applications, yet evaluating them is still challenging. Existing benchmarks are either manually constructed or are automatic, but lack the ability to evaluate the thought process of LLMs with arbitrary complexity. We contend that utilizing existing relational databases based on the entity-relationship (ER) model is a promising approach for constructing benchmarks as they contain structured knowledge that can be used to question LLMs. Unlike knowledge graphs, which are also used to evaluate LLMs, relational databases have integrity constraints that can be used to better construct complex in-depth questions and verify answers: (1) functional dependencies can be used to pinpoint critical keywords that an LLM must know to properly answer a given question containing certain attribute values; and (2) foreign key constraints can be used to join relations and construct multi-hop questions, which can be arbitrarily long and used to debug intermediate answers. We thus propose ERBench, which uses these integrity constraints to convert any database into an LLM benchmark. ERBench supports continuous evaluation as databases change, multimodal questions, and various prompt engineering techniques. In our experiments, we construct LLM benchmarks using databases of multiple domains and make an extensive comparison of contemporary LLMs. We show how ERBench can properly evaluate any LLM by not only checking for answer correctness, but also effectively verifying the rationales by looking for the right keywords.

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NeurIPS Conference 2024 Conference Paper

Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations

  • Hao Chen
  • Ankit Shah
  • Jindong Wang
  • Ran Tao
  • Yidong Wang
  • Xiang Li
  • Xing Xie
  • Masashi Sugiyama

Learning with reduced labeling standards, such as noisy label, partial label, and supplementary unlabeled data, which we generically refer to as imprecise label, is a commonplace challenge in machine learning tasks. Previous methods tend to propose specific designs for every emerging imprecise label configuration, which is usually unsustainable when multiple configurations of imprecision coexist. In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations. ILL leverages expectation-maximization (EM) for modeling the imprecise label information, treating the precise labels as latent variables. Instead of approximating the correct labels for training, it considers the entire distribution of all possible labeling entailed by the imprecise information. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings, with closed-form learning objectives derived from the unified EM modeling. Notably, ILL surpasses the existing specified techniques for handling imprecise labels, marking the first practical and unified framework with robust and effective performance across various challenging settings. We hope our work will inspire further research on this topic, unleashing the full potential of ILL in wider scenarios where precise labels are expensive and complicated to obtain.

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IJCAI Conference 2024 Conference Paper

On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models

  • Xinpeng Wang
  • Shitong Duan
  • Xiaoyuan Yi
  • Jing Yao
  • Shanlin Zhou
  • Zhihua Wei
  • Peng Zhang
  • Dongkuan Xu

Big models have achieved revolutionary breakthroughs in the field of AI, but they also pose potential ethical and societal risks to humans. Addressing such problems, alignment technologies were introduced to make these models conform to human preferences and values. Despite the considerable advancements in the past year, various challenges lie in establishing the optimal alignment strategy, such as data cost and scalable oversight, and how to align remains an open question. In this survey paper, we comprehensively investigate value alignment approaches. We first unpack the historical context of alignment tracing back to the 1920s (where it comes from), then delve into the mathematical essence of alignment (what it is), shedding light on the inherent challenges. Following this foundation, we provide a detailed examination of existing alignment methods, which fall into three categories: RL-based Alignment, SFT-based Alignment, and Inference-Time Alignment, and demonstrate their intrinsic connections, strengths, and limitations, helping readers better understand this research area. In addition, two emerging topics, alignment goal and multimodal alignment, are also discussed as novel frontiers in the field. Looking forward, we discuss potential alignment paradigms and how they could handle remaining challenges, prospecting where future alignment will go.

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JMLR Journal 2024 Journal Article

PromptBench: A Unified Library for Evaluation of Large Language Models

  • Kaijie Zhu
  • Qinlin Zhao
  • Hao Chen
  • Jindong Wang
  • Xing Xie

The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks. In this paper, we introduce PromptBench, a unified library to evaluate LLMs. It consists of several key components that can be easily used and extended by researchers: prompt construction, prompt engineering, dataset and model loading, adversarial prompt attack, dynamic evaluation protocols, and analysis tools. PromptBench is designed as an open, general, and flexible codebase for research purpose. It aims to facilitate original study in creating new benchmarks, deploying downstream applications, and designing new evaluation protocols. The code is available at: https://github.com/microsoft/promptbench and will be continuously supported. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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NeurIPS Conference 2023 Conference Paper

A Comprehensive Study on Text-attributed Graphs: Benchmarking and Rethinking

  • Hao Yan
  • Chaozhuo Li
  • Ruosong Long
  • Chao Yan
  • Jianan Zhao
  • Wenwen Zhuang
  • Jun Yin
  • Peiyan Zhang

Text-attributed graphs (TAGs) are prevalent in various real-world scenarios, where each node is associated with a text description. The cornerstone of representation learning on TAGs lies in the seamless integration of textual semantics within individual nodes and the topological connections across nodes. Recent advancements in pre-trained language models (PLMs) and graph neural networks (GNNs) have facilitated effective learning on TAGs, garnering increased research interest. However, the absence of meaningful benchmark datasets and standardized evaluation procedures for TAGs has impeded progress in this field. In this paper, we propose CS-TAG, a comprehensive and diverse collection of challenging benchmark datasets for TAGs. The CS-TAG datasets are notably large in scale and encompass a wide range of domains, spanning from citation networks to purchase graphs. In addition to building the datasets, we conduct extensive benchmark experiments over CS-TAG with various learning paradigms, including PLMs, GNNs, PLM-GNN co-training methods, and the proposed novel topological pre-training of language models. In a nutshell, we provide an overview of the CS-TAG datasets, standardized evaluation procedures, and present baseline experiments. The entire CS-TAG project is publicly accessible at \url{https: //github. com/sktsherlock/TAG-Benchmark}.

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NeurIPS Conference 2023 Conference Paper

Bayesian Active Causal Discovery with Multi-Fidelity Experiments

  • Zeyu Zhang
  • Chaozhuo Li
  • Xu Chen
  • Xing Xie

This paper studies the problem of active causal discovery when the experiments can be done based on multi-fidelity oracles, where higher fidelity experiments are more precise and expensive, while the lower ones are cheaper but less accurate. In this paper, we formally define the task of multi-fidelity active causal discovery, and design a probabilistic model for solving this problem. In specific, we first introduce a mutual-information based acquisition function to determine which variable should be intervened at which fidelity, and then a cascading model is proposed to capture the correlations between different fidelity oracles. Beyond the above basic framework, we also extend it to the batch intervention scenario. We find that the theoretical foundations behind the widely used and efficient greedy method do not hold in our problem. To solve this problem, we introduce a new concept called $\epsilon$-submodular, and design a constraint based fidelity model to theoretically validate the greedy method. We conduct extensive experiments to demonstrate the effectiveness of our model.

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TIST Journal 2023 Journal Article

CDSM: C ascaded D eep S emantic M atching on Textual Graphs Leveraging Ad-hoc Neighbor Selection

  • Jing Yao
  • Zheng Liu
  • Junhan Yang
  • Zhicheng Dou
  • Xing Xie
  • Ji-Rong Wen

Deep semantic matching aims at discriminating the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents with a graph structure, then leverage both the intrinsic document features and the extrinsic neighbor features to derive discrimination. Most of the existing works mainly care about how to utilize the presented neighbors, whereas limited effort is made to filter appropriate neighbors. We argue that the neighbor features could be highly noisy and partially useful. Thus, a lack of effective neighbor selection will not only incur a great deal of unnecessary computation cost but also restrict the matching accuracy severely. In this work, we propose a novel framework, C ascaded D eep S emantic M atching ( CDSM ), for accurate and efficient semantic matching on textual graphs. CDSM is highlighted for its two-stage workflow. In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost. We design both one-step and multi-step selection methods. In the second stage, a high-capacity graph-based matching network is employed to compute fine-grained relevance scores based on the well-selected neighbors. It is worth noting that CDSM is a generic framework which accommodates most of the mainstream graph-based semantic matching networks. The major challenge is how the selector can learn to discriminate the neighbors’ usefulness which has no explicit labels. To cope with this problem, we design a weak-supervision strategy for optimization, where we train the graph-based matching network at first and then the ad-hoc neighbor selector is learned on top of the annotations from the matching network. We conduct extensive experiments with three large-scale datasets, showing that CDSM notably improves the semantic matching accuracy and efficiency thanks to the selection of high-quality neighbors. The source code is released at https://github.com/jingjyyao/CDSM.

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NeurIPS Conference 2023 Conference Paper

Cross-links Matter for Link Prediction: Rethinking the Debiased GNN from a Data Perspective

  • Zihan Luo
  • Hong Huang
  • Jianxun Lian
  • Xiran Song
  • Xing Xie
  • Hai Jin

Recently, the bias-related issues in GNN-based link prediction have raised widely spread concerns. In this paper, we emphasize the bias on links across different node clusters, which we call cross-links, after considering its significance in both easing information cocoons and preserving graph connectivity. Instead of following the objective-oriented mechanism in prior works with compromised utility, we empirically find that existing GNN models face severe data bias between internal-links (links within the same cluster) and cross-links, and this inspires us to rethink the bias issue on cross-links from a data perspective. Specifically, we design a simple yet effective twin-structure framework, which can be easily applied to most of GNNs to mitigate the bias as well as boost their utility in an end-to-end manner. The basic idea is to generate debiased node embeddings as demonstrations, and fuse them into the embeddings of original GNNs. In particular, we learn debiased node embeddings with the help of augmented supervision signals, and a novel dynamic training strategy is designed to effectively fuse debiased node embeddings with the original node embeddings. Experiments on three datasets with six common GNNs show that our framework can not only alleviate the bias between internal-links and cross-links, but also boost the overall accuracy. Comparisons with other state-of-the-art methods also verify the superiority of our method.

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IJCAI Conference 2023 Conference Paper

FedSampling: A Better Sampling Strategy for Federated Learning

  • Tao Qi
  • Fangzhao Wu
  • Lingjuan Lyu
  • Yongfeng Huang
  • Xing Xie

Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for local model learning in each round. However, different clients may have significantly different data sizes, and the clients with more data cannot have more opportunities to contribute to model training, which may lead to inferior performance. In this paper, instead of client uniform sampling, we propose a novel data uniform sampling strategy for federated learning (FedSampling), which can effectively improve the performance of federated learning especially when client data size distribution is highly imbalanced across clients. In each federated learning round, local data on each client is randomly sampled for local model learning according to a probability based on the server desired sample size and the total sample size on all available clients. Since the data size on each client is privacy-sensitive, we propose a privacy-preserving way to estimate the total sample size with a differential privacy guarantee. Experiments on four benchmark datasets show that FedSampling can effectively improve the performance of federated learning.

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IJCAI Conference 2023 Conference Paper

KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation

  • Yuxi Feng
  • Xiaoyuan Yi
  • Laks V. S. Lakshmanan
  • Xing Xie

Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning. Nevertheless, in facilitating semi-supervised controllable language generation, ST faces two key challenges. First, augmented by self-generated pseudo text, generation models tend to over-exploit the previously learned text distribution, suffering from mode collapse and poor generation diversity. Second, generating pseudo text in each iteration is time-consuming, severely decelerating the training process. In this work, we propose KEST, a novel and efficient self-training framework to handle these problems. KEST utilizes a kernel-based loss, rather than standard cross entropy, to learn from the soft pseudo text produced by a shared non-autoregressive generator. We demonstrate both theoretically and empirically that KEST can benefit from more diverse pseudo text in an efficient manner, which allows not only refining and exploiting the previously fitted distribution but also enhanced exploration towards a larger potential text space, providing a guarantee of improved performance. Experiments on three controllable generation tasks demonstrate that KEST significantly improves control accuracy while maintaining comparable text fluency and generation diversity against several strong baselines.

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NeurIPS Conference 2023 Conference Paper

Model-enhanced Vector Index

  • Hailin Zhang
  • Yujing Wang
  • Qi Chen
  • Ruiheng Chang
  • Ting Zhang
  • Ziming Miao
  • Yingyan Hou
  • Yang Ding

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions.

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AAAI Conference 2023 Conference Paper

Prototypical Fine-Tuning: Towards Robust Performance under Varying Data Sizes

  • Yiqiao Jin
  • Xiting Wang
  • Yaru Hao
  • Yizhou Sun
  • Xing Xie

In this paper, we move towards combining large parametric models with non-parametric prototypical networks. We propose prototypical fine-tuning, a novel prototypical framework for fine-tuning pretrained language models (LM), which automatically learns a bias to improve predictive performance for varying data sizes, especially low-resource settings. Our prototypical fine-tuning approach can automatically adjust the model capacity according to the number of data points and the model's inherent attributes. Moreover, we propose four principles for effective prototype fine-tuning towards the optimal solution. Experimental results across various datasets show that our work achieves significant performance improvements under various low-resource settings, as well as comparable and usually better performances in high-resource scenarios.

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NeurIPS Conference 2023 Conference Paper

V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs

  • Senzhang Wang
  • Jun Yin
  • Chaozhuo Li
  • Xing Xie
  • Jianxin Wang

GNN explanation method aims to identify an explanatory subgraph which contains the most informative components of the full graph. However, a major limitation of existing GNN explainers is that they are not robust to the structurally corrupted graphs, e. g. , graphs with noisy or adversarial edges. On the one hand, existing GNN explainers mostly explore explanations based on either the raw graph features or the learned latent representations, both of which can be easily corrupted. On the other hand, the corruptions in graphs are irregular in terms of the structural properties, e. g. , the size or connectivity of graphs, which makes the rigorous constraints used by previous GNN explainers unfeasible. To address these issues, we propose a robust GNN explainer called V-InfoR. Specifically, a robust graph representation extractor, which takes insights of variational inference, is proposed to infer the latent distribution of graph representations. Instead of directly using the corrupted raw features or representations of each single graph, we sample the graph representations from the inferred distribution for the downstream explanation generator, which can effectively eliminate the minor corruption. We next formulate the explanation exploration as a graph information bottleneck (GIB) optimization problem. As a more general method that does not need any rigorous structural constraints, our GIB-based method can adaptively capture both the regularity and irregularity of the severely corrupted graphs for explanation. Extensive evaluations on both synthetic and real-world datasets indicate that V-InfoR significantly improves the GNN explanation performance for the structurally corrupted graphs. Code and dataset are available at https: //anonymous. 4open. science/r/V-InfoR-EF88

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NeurIPS Conference 2022 Conference Paper

A Neural Corpus Indexer for Document Retrieval

  • Yujing Wang
  • Yingyan Hou
  • Haonan Wang
  • Ziming Miao
  • Shibin Wu
  • Qi Chen
  • Yuqing Xia
  • Chengmin Chi

Current state-of-the-art document retrieval solutions mainly follow an index-retrieve paradigm, where the index is hard to be directly optimized for the final retrieval target. In this paper, we aim to show that an end-to-end deep neural network unifying training and indexing stages can significantly improve the recall performance of traditional methods. To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query. To optimize the recall performance of NCI, we invent a prefix-aware weight-adaptive decoder architecture, and leverage tailored techniques including query generation, semantic document identifiers, and consistency-based regularization. Empirical studies demonstrated the superiority of NCI on two commonly used academic benchmarks, achieving +21. 4% and +16. 8% relative enhancement for Recall@1 on NQ320k dataset and R-Precision on TriviaQA dataset, respectively, compared to the best baseline method.

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IJCAI Conference 2022 Conference Paper

Clickbait Detection via Contrastive Variational Modelling of Text and Label

  • Xiaoyuan Yi
  • Jiarui Zhang
  • Wenhao Li
  • Xiting Wang
  • Xing Xie

Clickbait refers to deliberately created sensational or deceptive text for tricking readers into clicking, which severely hurts the web ecosystem. With a growing number of clickbaits on social media, developing automatic detection methods becomes essential. Nonetheless, the performance of existing neural classifiers is limited due to the underutilization of small labelled datasets. Inspired by related pedagogy theories that learning to write can promote comprehension ability, we propose a novel Contrastive Variational Modelling (CVM) framework to exploit the labelled data better. CVM models the conditional distributions of text and clickbait labels by predicting labels from text and generating text from labels simultaneously with Variational AutoEncoder and further differentiates the learned spaces under each label by a mixed contrastive learning loss. In this way, CVM can capture more underlying textual properties and hence utilize label information to its full potential, boosting detection performance. We theoretically demonstrate CVM as learning a joint distribution of text, clickbait label, and latent variable. Experiments on three clickbait detection datasets show our method's robustness to inadequate and biased labels, outperforming several recent strong baselines.

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TMLR Journal 2022 Journal Article

Domain-invariant Feature Exploration for Domain Generalization

  • Wang Lu
  • Jindong Wang
  • Haoliang Li
  • Yiqiang Chen
  • Xing Xie

Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (DG) enables a model to generalize to an unseen test distribution, i.e., to learn domain-invariant representations. In this paper, we argue that domain-invariant features should be originating from both internal and mutual sides. Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i.e., the property within a domain, which is agnostic to other domains. Mutual invariance means that the features can be learned with multiple domains (cross-domain) and the features contain common information, i.e., the transferable features w.r.t. other domains. We then propose DIFEX for Domain-Invariant Feature EXploration. DIFEX employs a knowledge distillation framework to capture the high-level Fourier phase as the internally-invariant features and learn cross-domain correlation alignment as the mutually-invariant features. We further design an exploration loss to increase the feature diversity for better generalization. Extensive experiments on both time-series and visual benchmarks demonstrate that the proposed DIFEX achieves state-of-the-art performance.

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NeurIPS Conference 2022 Conference Paper

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

  • Tao Qi
  • Fangzhao Wu
  • Chuhan Wu
  • Lingjuan Lyu
  • Tong Xu
  • Hao Liao
  • Zhongliang Yang
  • Yongfeng Huang

Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving way. Since in real-world applications the data may contain bias on fairness-sensitive features (e. g. , gender), VFL models may inherit bias from training data and become unfair for some user groups. However, existing fair machine learning methods usually rely on the centralized storage of fairness-sensitive features to achieve model fairness, which are usually inapplicable in federated scenarios. In this paper, we propose a fair vertical federated learning framework (FairVFL), which can improve the fairness of VFL models. The core idea of FairVFL is to learn unified and fair representations of samples based on the decentralized feature fields in a privacy-preserving way. Specifically, each platform with fairness-insensitive features first learns local data representations from local features. Then, these local representations are uploaded to a server and aggregated into a unified representation for the target task. In order to learn a fair unified representation, we send it to each platform storing fairness-sensitive features and apply adversarial learning to remove bias from the unified representation inherited from the biased data. Moreover, for protecting user privacy, we further propose a contrastive adversarial learning method to remove private information from the unified representation in server before sending it to the platforms keeping fairness-sensitive features. Experiments on three real-world datasets validate that our method can effectively improve model fairness with user privacy well-protected.

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TIST Journal 2022 Journal Article

FedCTR: Federated Native Ad CTR Prediction with Cross-platform User Behavior Data

  • Chuhan Wu
  • Fangzhao Wu
  • Lingjuan Lyu
  • Yongfeng Huang
  • Xing Xie

Native ad is a popular type of online advertisement that has similar forms with the native content displayed on websites. Native ad click-through rate (CTR) prediction is useful for improving user experience and platform revenue. However, it is challenging due to the lack of explicit user intent, and user behaviors on the platform with native ads may be insufficient to infer users’ interest in ads. Fortunately, user behaviors exist on many online platforms that can provide complementary information for user-interest mining. Thus, leveraging multi-platform user behaviors is useful for native ad CTR prediction. However, user behaviors are highly privacy-sensitive, and the behavior data on different platforms cannot be directly aggregated due to user privacy concerns and data protection regulations. Existing CTR prediction methods usually require centralized storage of user behavior data for user modeling, which cannot be directly applied to the CTR prediction task with multi-platform user behaviors. In this article, we propose a federated native ad CTR prediction method named FedCTR, which can learn user-interest representations from cross-platform user behaviors in a privacy-preserving way. On each platform a local user model learns user embeddings from the local user behaviors on that platform. The local user embeddings from different platforms are uploaded to a server for aggregation, and the aggregated ones are sent to the ad platform for CTR prediction. Besides, we apply local differential privacy and differential privacy to the local and aggregated user embeddings, respectively, for better privacy protection. Moreover, we propose a federated framework for collaborative model training with distributed models and user behaviors. Extensive experiments on real-world dataset show that FedCTR can effectively leverage multi-platform user behaviors for native ad CTR prediction in a privacy-preserving manner.

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NeurIPS Conference 2022 Conference Paper

Self-explaining deep models with logic rule reasoning

  • Seungeon Lee
  • Xiting Wang
  • Sungwon Han
  • Xiaoyuan Yi
  • Xing Xie
  • Meeyoung Cha

We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision. By “human precision”, we refer to the degree to which humans agree with the reasons models provide for their predictions. Human precision affects user trust and allows users to collaborate closely with the model. We demonstrate that logic rule explanations naturally satisfy them with the expressive power required for good predictive performance. We then illustrate how to enable a deep model to predict and explain with logic rules. Our method does not require predefined logic rule sets or human annotations and can be learned efficiently and easily with widely-used deep learning modules in a differentiable way. Extensive experiments show that our method gives explanations closer to human decision logic than other methods while maintaining the performance of the deep learning model.

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AAAI Conference 2022 Conference Paper

Towards Fine-Grained Reasoning for Fake News Detection

  • Yiqiao Jin
  • Xiting Wang
  • Ruichao Yang
  • Yizhou Sun
  • Wei Wang
  • Hao Liao
  • Xing Xie

The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues. In this paper, we move towards fine-grained reasoning for fake news detection by better reflecting the logical processes of human thinking and enabling the modeling of subtle clues. In particular, we propose a fine-grained reasoning framework by following the human’s information-processing model, introduce a mutualreinforcement-based method for incorporating human knowledge about which evidence is more important, and design a prior-aware bi-channel kernel graph network to model subtle differences between pieces of evidence. Extensive experiments show that our model outperforms the state-of-the-art methods and demonstrate the explainability of our approach.

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NeurIPS Conference 2022 Conference Paper

USB: A Unified Semi-supervised Learning Benchmark for Classification

  • Yidong Wang
  • Hao Chen
  • Yue Fan
  • Wang Sun
  • Ran Tao
  • Wenxin Hou
  • Renjie Wang
  • Linyi Yang

Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation protocols are often constrained to computer vision (CV) tasks. In addition, previous work typically trains deep neural networks from scratch, which is time-consuming and environmentally unfriendly. To address the above issues, we construct a Unified SSL Benchmark (USB) for classification by selecting 15 diverse, challenging, and comprehensive tasks from CV, natural language processing (NLP), and audio processing (Audio), on which we systematically evaluate the dominant SSL methods, and also open-source a modular and extensible codebase for fair evaluation of these SSL methods. We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning. USB enables the evaluation of a single SSL algorithm on more tasks from multiple domains but with less cost. Specifically, on a single NVIDIA V100, only 39 GPU days are required to evaluate FixMatch on 15 tasks in USB while 335 GPU days (279 GPU days on 4 CV datasets except for ImageNet) are needed on 5 CV tasks with TorchSSL.

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AAAI Conference 2021 Conference Paper

Fairness-aware News Recommendation with Decomposed Adversarial Learning

  • Chuhan Wu
  • Fangzhao Wu
  • Xiting Wang
  • Yongfeng Huang
  • Xing Xie

News recommendation is important for online news services. Existing news recommendation models are usually learned from users’ news click behaviors. Usually the behaviors of users with the same sensitive attributes (e. g. , genders) have similar patterns and news recommendation models can easily capture these patterns. It may lead to some biases related to sensitive user attributes in the recommendation results, e. g. , always recommending sports news to male users, which is unfair since users may not receive diverse news information. In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes. In our approach, we propose to decompose the user interest model into two components. One component aims to learn a bias-aware user embedding that captures the bias information on sensitive user attributes, and the other aims to learn a bias-free user embedding that only encodes attribute-independent user interest information for fairnessaware news recommendation. In addition, we propose to apply an attribute prediction task to the bias-aware user embedding to enhance its ability on bias modeling, and we apply adversarial learning to the bias-free user embedding to remove the bias information from it. Moreover, we propose an orthogonality regularization method to encourage the bias-free user embeddings to be orthogonal to the bias-aware one to better distinguish the bias-free user embedding from the bias-aware one. For fairness-aware news ranking, we only use the biasfree user embedding. Extensive experiments on benchmark dataset show that our approach can effectively improve fairness in news recommendation with minor performance loss.

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NeurIPS Conference 2021 Conference Paper

GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph

  • Junhan Yang
  • Zheng Liu
  • Shitao Xiao
  • Chaozhuo Li
  • Defu Lian
  • Sanjay Agrawal
  • Amit Singh
  • Guangzhong Sun

The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information. Recent breakthroughs on pretrained language models and graph neural networks push forward the development of corresponding techniques. The existing works mainly rely on the cascaded model architecture: the textual features of nodes are independently encoded by language models at first; the textual embeddings are aggregated by graph neural networks afterwards. However, the above architecture is limited due to the independent modeling of textual features. In this work, we propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow, making each node's semantic accurately comprehended from the global perspective. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph. Extensive evaluations are conducted on three large-scale benchmark datasets, where GraphFormers outperform the SOTA baselines with comparable running efficiency. The source code is released at https: //github. com/microsoft/GraphFormers.

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IJCAI Conference 2021 Conference Paper

Learning Groupwise Explanations for Black-Box Models

  • Jingyue Gao
  • Xiting Wang
  • Yasha Wang
  • Yulan Yan
  • Xing Xie

We study two user demands that are important during the exploitation of explanations in practice: 1) understanding the overall model behavior faithfully with limited cognitive load and 2) predicting the model behavior accurately on unseen instances. We illustrate that the two user demands correspond to two major sub-processes in the human cognitive process and propose a unified framework to fulfill them simultaneously. Given a local explanation method, our framework jointly 1) learns a limited number of groupwise explanations that interpret the model behavior on most instances with high fidelity and 2) specifies the region where each explanation applies. Experiments on six datasets demonstrate the effectiveness of our method.

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IJCAI Conference 2021 Conference Paper

User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation

  • Chuhan Wu
  • Fangzhao Wu
  • Yongfeng Huang
  • Xing Xie

Accurate user modeling is critical for news recommendation. Existing news recommendation methods usually model users' interest from their behaviors via sequential or attentive models. However, they cannot model the rich relatedness between user behaviors, which can provide useful contexts of these behaviors for user interest modeling. In this paper, we propose a novel user modeling approach for news recommendation, which models each user as a personalized heterogeneous graph built from user behaviors to better capture the fine-grained behavior relatedness. In addition, in order to learn user interest embedding from the personalized heterogeneous graph, we propose a novel heterogeneous graph pooling method, which can summarize both node features and graph topology, and be aware of the varied characteristics of different types of nodes. Experiments on large-scale benchmark dataset show the proposed methods can effectively improve the performance of user modeling for news recommendation.

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NeurIPS Conference 2020 Conference Paper

Sampling-Decomposable Generative Adversarial Recommender

  • Binbin Jin
  • Defu Lian
  • Zheng Liu
  • Qi Liu
  • Jianhui Ma
  • Xing Xie
  • Enhong Chen

Recommendation techniques are important approaches for alleviating information overload. Being often trained on implicit user feedback, many recommenders suffer from the sparsity challenge due to the lack of explicitly negative samples. The GAN-style recommenders (i. e. , IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective. However, producing samples from the generator is very time-consuming, and our empirical study shows that the discriminator performs poor in top-k item recommendation. To this end, a theoretical analysis is made for the GAN-style algorithms, showing that the generator of limit capacity is diverged from the optimal generator. This may interpret the limitation of discriminator's performance. Based on these findings, we propose a Sampling-Decomposable Generative Adversarial Recommender (SD-GAR). In the framework, the divergence between some generator and the optimum is compensated by self-normalized importance sampling; the efficiency of sample generation is improved with a sampling-decomposable generator, such that each sample can be generated in O(1) with the Vose-Alias method. Interestingly, due to decomposability of sampling, the generator can be optimized with the closed-form solutions in an alternating manner, being different from policy gradient in the GAN-style algorithms. We extensively evaluate the proposed algorithm with five real-world recommendation datasets. The results show that SD-GAR outperforms IRGAN by 12. 4% and the SOTA recommender by 10% on average. Moreover, discriminator training can be 20x faster on the dataset with more than 120K items.

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IJCAI Conference 2020 Conference Paper

Towards Explainable Conversational Recommendation

  • Zhongxia Chen
  • Xiting Wang
  • Xing Xie
  • Mehul Parsana
  • Akshay Soni
  • Xiang Ao
  • Enhong Chen

Recent studies have shown that both accuracy and explainability are important for recommendation. In this paper, we introduce explainable conversational recommendation, which enables incremental improvement of both recommendation accuracy and explanation quality through multi-turn user-model conversation. We show how the problem can be formulated, and design an incremental multi-task learning framework that enables tight collaboration between recommendation prediction, explanation generation, and user feedback integration. We also propose a multi-view feedback integration method to enable effective incremental model update. Empirical results demonstrate that our model not only consistently improves the recommendation accuracy but also generates explanations that fit user interests reflected in the feedbacks.

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IJCAI Conference 2019 Conference Paper

Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation

  • Zeping Yu
  • Jianxun Lian
  • Ahmad Mahmoody
  • Gongshen Liu
  • Xing Xie

User modeling is an essential task for online recommender systems. In the past few decades, collaborative filtering (CF) techniques have been well studied to model users' long term preferences. Recently, recurrent neural networks (RNN) have shown a great advantage in modeling users' short term preference. A natural way to improve the recommender is to combine both long-term and short-term modeling. Previous approaches neglect the importance of dynamically integrating these two user modeling paradigms. Moreover, users' behaviors are much more complex than sentences in language modeling or images in visual computing, thus the classical structures of RNN such as Long Short-Term Memory (LSTM) need to be upgraded for better user modeling. In this paper, we improve the traditional RNN structure by proposing a time-aware controller and a content-aware controller, so that contextual information can be well considered to control the state transition. We further propose an attention-based framework to combine users' long-term and short-term preferences, thus users' representation can be generated adaptively according to the specific context. We conduct extensive experiments on both public and industrial datasets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.

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IJCAI Conference 2019 Conference Paper

Co-Attentive Multi-Task Learning for Explainable Recommendation

  • Zhongxia Chen
  • Xiting Wang
  • Xing Xie
  • Tong Wu
  • Guoqing Bu
  • Yining Wang
  • Enhong Chen

Despite widespread adoption, recommender systems remain mostly black boxes. Recently, providing explanations about why items are recommended has attracted increasing attention due to its capability to enhance user trust and satisfaction. In this paper, we propose a co-attentive multi-task learning model for explainable recommendation. Our model improves both prediction accuracy and explainability of recommendation by fully exploiting the correlations between the recommendation task and the explanation task. In particular, we design an encoder-selector-decoder architecture inspired by human's information-processing model in cognitive psychology. We also propose a hierarchical co-attentive selector to effectively model the cross knowledge transferred for both tasks. Our model not only enhances prediction accuracy of the recommendation task, but also generates linguistic explanations that are fluent, useful, and highly personalized. Experiments on three public datasets demonstrate the effectiveness of our model.

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IJCAI Conference 2019 Conference Paper

Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems

  • Xiao Zhou
  • Danyang Liu
  • Jianxun Lian
  • Xing Xie

The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple types of user feedback provide us with tremendous opportunities to detect individuals' fine-grained preferences. Different from most existing recommender systems that rely on a single type of feedback, we advocate incorporating multiple types of user-item interactions for better recommendations. Based on the observation that the underlying spectrum of user preferences is reflected in various types of interactions with items and can be uncovered by latent relational learning in metric space, we propose a unified neural learning framework, named Multi-Relational Memory Network (MRMN). It can not only model fine-grained user-item relations but also enable us to discriminate between feedback types in terms of the strength and diversity of user preferences. Extensive experiments show that the proposed MRMN model outperforms competitive state-of-the-art algorithms in a wide range of scenarios, including e-commerce, local services, and job recommendations.

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AAAI Conference 2019 Conference Paper

DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching

  • Kun Zhang
  • Guangyi Lv
  • Linyuan Wang
  • Le Wu
  • Enhong Chen
  • Fangzhao Wu
  • Xing Xie

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all matching methods, attention mechanism plays an important role in capturing the semantic relations and properly aligning the elements of two sentences. Previous methods utilized attention mechanism to select important parts of sentences at one time. However, the important parts of the sentence during semantic matching are dynamically changing with the degree of sentence understanding. Selecting the important parts at one time may be insufficient for semantic understanding. To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding. To be specific, we first employ Attention Stack-GRU (ASG) unit to model the original sentence repeatedly and preserve all the information from bottom-most word embedding input to up-most recurrent output. Second, we utilize Dynamic Re-read (DRr) unit to pay close attention to one important word at one time with the consideration of learned information and re-read the important words for better sentence semantic understanding. Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. In addition, it is very interesting that some of finding in our experiments are consistent with the findings of psychological research.

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AAAI Conference 2019 Conference Paper

Explainable Recommendation through Attentive Multi-View Learning

  • Jingyue Gao
  • Xiting Wang
  • Yasha Wang
  • Xing Xie

Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods. The basic idea is to build an initial network based on an explainable deep hierarchy (e. g. , Microsoft Concept Graph) and improve the model accuracy by optimizing key variables in the hierarchy (e. g. , node importance and relevance). To ensure accurate rating prediction, we propose an attentive multi-view learning framework. The framework enables us to handle sparse and noisy data by co-regularizing among different feature levels and combining predictions attentively. To mine readable explanations from the hierarchy, we formulate personalized explanation generation as a constrained tree node selection problem and propose a dynamic programming algorithm to solve it. Experimental results show that our model outperforms state-of-the-art methods in terms of both accuracy and explainability.

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IJCAI Conference 2019 Conference Paper

Hi-Fi Ark: Deep User Representation via High-Fidelity Archive Network

  • Zheng Liu
  • Yu Xing
  • Fangzhao Wu
  • Mingxiao An
  • Xing Xie

Deep learning techniques have been widely applied to modern recommendation systems, bringing in flexible and effective ways of user representation. Conventionally, user representations are generated purely in the offline stage. Without referencing to the specific candidate item for recommendation, it is difficult to fully capture user preference from the perspective of interest. More recent algorithms tend to generate user representation at runtime, where user's historical behaviors are attentively summarized w. r. t. the presented candidate item. In spite of the improved efficacy, it is too expensive for many real-world scenarios because of the repetitive access to user's entire history. In this work, a novel user representation framework, Hi-Fi Ark, is proposed. With Hi-Fi Ark, user history is summarized into highly compact and complementary vectors in the offline stage, known as archives. Meanwhile, user preference towards a specific candidate item can be precisely captured via the attentive aggregation of such archives. As a result, both deployment feasibility and superior recommendation efficacy are achieved by Hi-Fi Ark. The effectiveness of Hi-Fi Ark is empirically validated on three real-world datasets, where remarkable and consistent improvements are made over a variety of well-recognized baseline methods.

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IJCAI Conference 2019 Conference Paper

Neural News Recommendation with Attentive Multi-View Learning

  • Chuhan Wu
  • Fangzhao Wu
  • Mingxiao An
  • Jianqiang Huang
  • Yongfeng Huang
  • Xing Xie

Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning is critical for news recommendation. Existing news recommendation methods usually learn these representations based on single news information, e. g. , title, which may be insufficient. In this paper we propose a neural news recommendation approach which can learn informative representations of users and news by exploiting different kinds of news information. The core of our approach is a news encoder and a user encoder. In the news encoder we propose an attentive multi-view learning model to learn unified news representations from titles, bodies and topic categories by regarding them as different views of news. In addition, we apply both word-level and view-level attention mechanism to news encoder to select important words and views for learning informative news representations. In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of news recommendation.

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IJCAI Conference 2019 Conference Paper

Personalized Multimedia Item and Key Frame Recommendation

  • Le Wu
  • Lei Chen
  • Yonghui Yang
  • Richang Hong
  • Yong Ge
  • Xing Xie
  • Meng Wang

When recommending or advertising items to users, an emerging trend is to present each multimedia item with a key frame image (e. g. , the poster of a movie). As each multimedia item can be represented as multiple fine-grained visual images (e. g. , related images of the movie), personalized key frame recommendation is necessary in these applications to attract users' unique visual preferences. However, previous personalized key frame recommendation models relied on users' fine grained image behavior of multimedia items (e. g. , user-image interaction behavior), which is often not available in real scenarios. In this paper, we study the general problem of joint multimedia item and key frame recommendation in the absence of the fine-grained user-image behavior. We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior. To tackle this challenge, we leverage users' item behavior by projecting users(items) in two latent spaces: a collaborative latent space and a visual latent space. We further design a model to discern both the collaborative and visual dimensions of users, and model how users make decisive item preferences from these two spaces. As a result, the learned user visual profiles could be directly applied for key frame recommendation. Finally, experimental results on a real-world dataset clearly show the effectiveness of our proposed model on the two recommendation tasks.

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TIST Journal 2019 Journal Article

Personalized Reason Generation for Explainable Song Recommendation

  • Guoshuai Zhao
  • Hao Fu
  • Ruihua Song
  • Tetsuya Sakai
  • Zhongxia Chen
  • Xing Xie
  • Xueming Qian

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as “Customers who bought this item also bought…”. Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. The goal of our research is to make the users feel as if they are receiving recommendations from their friends. To this end, we formulate a new challenging problem called personalized reason generation for explainable recommendation for songs in conversation applications and propose a solution that generates a natural language explanation of the reason for recommending a song to that particular user. For example, if the user is a student, our method can generate an output such as “Campus radio plays this song at noon every day, and I think it sounds wonderful,” which the student may find easy to relate to. In the offline experiments, through manual assessments, the gain of our method is statistically significant on the relevance to songs and personalization to users comparing with baselines. Large-scale online experiments show that our method outperforms manually selected reasons by 8.2% in terms of click-through rate. Evaluation results indicate that our generated reasons are relevant to songs and personalized to users, and they attract users to click the recommendations.

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AAAI Conference 2019 Conference Paper

Session-Based Recommendation with Graph Neural Networks

  • Shu Wu
  • Yuyuan Tang
  • Yanqiao Zhu
  • Liang Wang
  • Xing Xie
  • Tieniu Tan

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.

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AAAI Conference 2018 Conference Paper

CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

  • Jingyuan Wang
  • Xu He
  • Ze Wang
  • Junjie Wu
  • Nicholas Jing Yuan
  • Xing Xie
  • Zhang Xiong

Driven by the wave of urbanization in recent decades, the research topic about migrant behavior analysis draws great attention from both academia and the government. Nevertheless, subject to the cost of data collection and the lack of modeling methods, most of existing studies use only questionnaire surveys with sparse samples and non-individual level statistical data to achieve coarse-grained studies of migrant behaviors. In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels. Specifically, CD-CNN features in decomposing the mobile data into location domain and communication domain, and adopts a joint learning framework that combines two convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN employs a three-step algorithm for training, in which the co-training step is of great value to partially supervised cross-domain learning. Comparative experiments on the city Wuxi demonstrate the high predictive power of CD-CNN. Two interesting applications further highlight the ability of CD-CNN for in-depth migrant behavioral analysis.

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AAAI Conference 2018 Conference Paper

GraphGAN: Graph Representation Learning With Generative Adversarial Nets

  • Hongwei Wang
  • Jia Wang
  • Jialin Wang
  • Miao Zhao
  • Weinan Zhang
  • Fuzheng Zhang
  • Xing Xie
  • Minyi Guo

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose Graph- GAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces “fake” samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that Graph- GAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.

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IJCAI Conference 2018 Conference Paper

Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data

  • Yingzi Wang
  • Xiao Zhou
  • Anastasios Noulas
  • Cecilia Mascolo
  • Xing Xie
  • Enhong Chen

Chronic diseases like cancer and diabetes are major threats to human life. Understanding the distribution and progression of chronic diseases of a population is important in assisting the allocation of medical resources as well as the design of policies in preemptive healthcare. Traditional methods to obtain large scale indicators on population health, e. g. , surveys and statistical analysis, can be costly and time-consuming and often lead to a coarse spatio-temporal picture. In this paper, we leverage a dataset describing the human mobility patterns of citizens in a large metropolitan area. By viewing local human lifestyles we predict the evolution rate of several chronic diseases at the level of a city neighborhood. We apply the combination of a collaborative topic modeling (CTM) and a Gaussian mixture method (GMM) to tackle the data sparsity challenge and achieve robust predictions on health conditions simultaneously. Our method enables the analysis and prediction of disease rate evolution at fine spatio-temporal scales and demonstrates the potential of incorporating datasets from mobile web sources to improve population health monitoring. Evaluations using real-world check-in and chronic disease morbidity datasets in the city of London show that the proposed CTM+GMM model outperforms various baseline methods.

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IJCAI Conference 2018 Conference Paper

Sequential Recommender System based on Hierarchical Attention Networks

  • Haochao Ying
  • Fuzhen Zhuang
  • Fuzheng Zhang
  • Yanchi Liu
  • Guandong Xu
  • Xing Xie
  • Hui Xiong
  • Jian Wu

With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or item-item interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones.

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IJCAI Conference 2018 Conference Paper

Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach

  • Jianxun Lian
  • Fuzheng Zhang
  • Xing Xie
  • Guangzhong Sun

Millions of news articles emerge every day. How to provide personalized news recommendations has become a critical task for service providers. In the past few decades, latent factor models has been widely used for building recommender systems (RSs). With the remarkable success of deep learning techniques especially in visual computing and natural language understanding, more and more researchers have been trying to leverage deep neural networks to learn latent representations for advanced RSs. Following mainstream deep learning-based RSs, we propose a novel deep fusion model (DFM), which aims to improve the representation learning abilities in deep RSs and can be used for both candidate retrieval and item re-ranking. There are two key components in our DFM approach, namely an inception module and an attention mechanism. The inception module improves the plain multi-layer network via leveraging of various levels of interaction simultaneously, while the attention mechanism merges latent representations learnt from different channels in a customized fashion. We conduct extensive experiments on a commercial news reading dataset, and the results demonstrate that the proposed DFM is superior to several state-of-the-art models.

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IJCAI Conference 2017 Conference Paper

App Download Forecasting: An Evolutionary Hierarchical Competition Approach

  • Yingzi Wang
  • Nicholas Jing Yuan
  • Yu Sun
  • Chuan Qin
  • Xing Xie

Product sales forecasting enables comprehensive understanding of products' future development, making it of particular interest for companies to improve their business, for investors to measure the values of firms, and for users to capture the trends of a market. Recent studies show that the complex competition interactions among products directly influence products' future development. However, most existing approaches fail to model the evolutionary competition among products and lack the capability to organically reflect multi-level competition analysis in sales forecasting. To address these problems, we propose the Evolutionary Hierarchical Competition Model (EHCM), which effectively considers the time-evolving multi-level competition among products. The EHCM model systematically integrates hierarchical competition analysis with multi-scale time series forecasting. Extensive experiments using a real-world app download dataset show that EHCM outperforms state-of-the-art methods in various forecasting granularities.

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TIST Journal 2017 Journal Article

Robust Spammer Detection in Microblogs

  • Hao Fu
  • Xing Xie
  • Yong Rui
  • Neil Zhenqiang Gong
  • Guangzhong Sun
  • Enhong Chen

Microblogging Web sites, such as Twitter and Sina Weibo, have become popular platforms for socializing and sharing information in recent years. Spammers have also discovered this new opportunity to unfairly overpower normal users with unsolicited content, namely social spams. Although it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users seeking spammers on purpose is also observed. We regard this behavior as useful information for spammer detection. In this article, we approach the problem of spammer detection by leveraging the “carefulness” of users, which indicates how careful a user is when she is about to follow a potential spammer. We propose a framework to measure the carefulness and develop a supervised learning algorithm to estimate it based on known spammers and legitimate users. We illustrate how the robustness of the detection algorithms can be improved with aid of the proposed measure. Evaluation on two real datasets from Sina Weibo and Twitter with millions of users are performed, as well as an online test on Sina Weibo. The results show that our approach indeed captures the carefulness, and it is effective for detecting spammers. In addition, we find that our measure is also beneficial for other applications, such as link prediction.

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IJCAI Conference 2017 Conference Paper

Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory

  • Chenyi Zhuang
  • Nicholas Jing Yuan
  • Ruihua Song
  • Xing Xie
  • Qiang Ma

Technologies are increasingly taking advantage of the explosion in the amount of data generated by social multimedia (e. g. , web searches, ad targeting, and urban computing). In this paper, we propose a multi-view learning framework for presenting the construction of a new urban movement knowledge graph, which could greatly facilitate the research domains mentioned above. In particular, by viewing GPS trajectory data from temporal, spatial, and spatiotemporal points of view, we construct a knowledge graph of which nodes and edges are their locations and relations, respectively. On the knowledge graph, both nodes and edges are represented in latent semantic space. We verify its utility by subsequently applying the knowledge graph to predict the extent of user attention (high or low) paid to different locations in a city. Experimental evaluations and analysis of a real-world dataset show significant improvements in comparison to state-of-the-art methods.

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AAAI Conference 2016 Conference Paper

Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds

  • Meng Jiang
  • Peng Cui
  • Nicholas Jing Yuan
  • Xing Xie
  • Shiqiang Yang

People often use multiple platforms to fulfill their different information needs. With the ultimate goal of serving people intelligently, a fundamental way is to get comprehensive understanding about user needs. How to organically integrate and bridge cross-platform information in a human-centric way is important. Existing transfer learning assumes either fullyoverlapped or non-overlapped among the users. However, the real case is the users of different platforms are partially overlapped. The number of overlapped users is often small and the explicitly known overlapped users is even less due to the lacking of unified ID for a user across different platforms. In this paper, we propose a novel semi-supervised transfer learning method to address the problem of cross-platform behavior prediction, called XPTRANS. To alleviate the sparsity issue, it fully exploits the small number of overlapped crowds to optimally bridge a user’s behaviors in different platforms. Extensive experiments across two real social networks show that XPTRANS significantly outperforms the state-of-the-art. We demonstrate that by fully exploiting 26% overlapped users, XPTRANS can predict the behaviors of non-overlapped users with the same accuracy as overlapped users, which means the small overlapped crowds can successfully bridge the information across different platforms.

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TIST Journal 2016 Journal Article

Prediction and Simulation of Human Mobility Following Natural Disasters

  • Xuan Song
  • Quanshi Zhang
  • Yoshihide Sekimoto
  • Ryosuke Shibasaki
  • Nicholas Jing Yuan
  • Xing Xie

In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users 1 over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.

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TIST Journal 2016 Journal Article

Relevance Meets Coverage

  • Le Wu
  • Qi Liu
  • Enhong Chen
  • Nicholas Jing Yuan
  • Guangming Guo
  • Xing Xie

Collaborative filtering (CF) models offer users personalized recommendations by measuring the relevance between the active user and each individual candidate item. Following this idea, user-based collaborative filtering (UCF) usually selects the local popular items from the like-minded neighbor users. However, these traditional relevance-based models only consider the individuals (i.e., each neighbor user and candidate item) separately during neighbor set selection and recommendation set generation, thus usually incurring highly similar recommendations that lack diversity. While many researchers have recognized the importance of diversified recommendations, the proposed solutions either needed additional semantic information of items or decreased accuracy in this process. In this article, we describe how to generate both accurate and diversified recommendations from a new perspective. Along this line, we first introduce a simple measure of coverage that quantifies the usefulness of the whole set, that is, the neighbor userset and the recommended itemset as a complete entity. Then we propose a recommendation framework named REC that considers both traditional relevance-based scores and the new coverage measure based on UCF. Under REC, we further prove that the goals of maximizing relevance and coverage measures simultaneously in both the neighbor set selection step and the recommendation set generation step are NP-hard. Luckily, we can solve them effectively and efficiently by exploiting the inherent submodular property. Furthermore, we generalize the coverage notion and the REC framework from both a data perspective and an algorithm perspective. Finally, extensive experimental results on three real-world datasets show that the REC-based recommendation models can naturally generate more diversified recommendations without decreasing accuracy compared to some state-of-the-art models.

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IJCAI Conference 2016 Conference Paper

Sparse Bayesian Content-Aware Collaborative Filtering for Implicit Feedback

  • Defu Lian
  • Yong Ge
  • Nicholas Jing Yuan
  • Xing Xie
  • Hui Xiong

The popularity of social media creates a large amount of user-generated content, playing an important role in addressing cold-start problems in recommendation. Although much effort has been devoted to incorporating this information into recommendation, past work mainly targets explicit feedback. There is still no general framework tailored to implicit feedback, such as views, listens, or visits. To this end, we propose a sparse Bayesian content-aware collaborative filtering framework especially for implicit feedback, and develop a scalable optimization algorithm to jointly learn latent factors and hyperparameters. Due to the adaptive update of hyperparameters, automatic feature selection is naturally embedded in this framework. Convincing experimental results on three different implicit feedback datasets indicate the superiority of the proposed algorithm to state-of-the-art content-aware recommendation methods.

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AAAI Conference 2015 Conference Paper

A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data

  • Xuan Song
  • Quanshi Zhang
  • Yoshihide Sekimoto
  • Ryosuke Shibasaki
  • Nicholas Jing Yuan
  • Xing Xie

The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, understanding and simulating of human emergency mobility following disasters will become the critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, due to the uniqueness of various disasters and the unavailability of reliable and large scale human mobility data, such kind of research is very difficult to be performed. Hence, in this paper, we collect big and heterogeneous data (e. g. 1. 6 million users’ GPS records in three years, 17520 times of Japan earthquake data in four years, news reporting data, transportation network data and etc.) to capture and analyze human emergency mobility following different disasters. By mining these big data, we aim to understand what basic laws govern human mobility following disasters, and develop a general model of human emergency mobility for generating and simulating large amount of human emergency movements. The experimental results and validations demonstrate the efficiency of our simulation model, and suggest that human mobility following disasters may be significantly more predictable and can be easier simulated than previously thought.

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TIST Journal 2015 Journal Article

CEPR

  • Defu Lian
  • Xing Xie
  • Vincent W. Zheng
  • Nicholas Jing Yuan
  • Fuzheng Zhang
  • Enhong Chen

With the growing popularity of location-based social networks, numerous location visiting records (e.g., check-ins) continue to accumulate over time. The more these records are collected, the better we can understand users’ mobility patterns and the more accurately we can predict their future locations. However, due to the personality trait of neophilia, people also show propensities of novelty seeking in human mobility, that is, exploring unvisited but tailored locations for them to visit. As such, the existing prediction algorithms, mainly relying on regular mobility patterns, face severe challenges because such behavior is beyond the reach of regularity. As a matter of fact, the prediction of this behavior not only relies on the forecast of novelty-seeking tendency but also depends on how to determine unvisited candidate locations. To this end, we put forward a Collaborative Exploration and Periodically Returning model (CEPR), based on a novel problem, Exploration Prediction (EP), which forecasts whether people will seek unvisited locations to visit, in the following. When people are predicted to do exploration, a state-of-the-art recommendation algorithm, armed with collaborative social knowledge and assisted by geographical influence, will be applied for seeking the suitable candidates; otherwise, a traditional prediction algorithm, incorporating both regularity and the Markov model, will be put into use for figuring out the most possible locations to visit. We then perform case studies on check-ins and evaluate them on two large-scale check-in datasets with 6M and 36M records, respectively. The evaluation results show that EP achieves a roughly 20% classification error rate on both datasets, greatly outperforming the baselines, and that CEPR improves performances by as much as 30% compared to the traditional location prediction algorithms.

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AAAI Conference 2015 Conference Paper

Content-Based Collaborative Filtering for News Topic Recommendation

  • Zhongqi Lu
  • Zhicheng Dou
  • Jianxun Lian
  • Xing Xie
  • Qiang Yang

News recommendation has become a big attraction with which major Web search portals retain their users. Two effective approaches are Content-based Filtering and Collaborative Filtering, each serving a specific recommendation scenario. The Content-based Filtering approaches inspect rich contexts of the recommended items, while the Collaborative Filtering approaches predict the interests of long-tail users by collaboratively learning from interests of related users. We have observed empirically that, for the problem of news topic displaying, both the rich context of news topics and the long-tail users exist. Therefore, in this paper, we propose a Content-based Collaborative Filtering approach (CCF) to bring both Content-based Filtering and Collaborative Filtering approaches together. We found that combining the two is not an easy task, but the benefits of CCF are impressive. On one hand, CCF makes recommendations based on the rich contexts of the news. On the other hand, CCF collaboratively analyzes the scarce feedbacks from the long-tail users. We tailored this CCF approach for the news topic displaying on the Bing front page and demonstrated great gains in attracting users. In the experiments and analyses part of this paper, we discuss the performance gains and insights in news topic recommendation in Bing.

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TIST Journal 2015 Journal Article

Effective Social Graph Deanonymization Based on Graph Structure and Descriptive Information

  • Hao Fu
  • Aston Zhang
  • Xing Xie

The study of online social networks has attracted increasing interest. However, concerns are raised for the privacy risks of user data since they have been frequently shared among researchers, advertisers, and application developers. To solve this problem, a number of anonymization algorithms have been recently developed for protecting the privacy of social graphs. In this article, we proposed a graph node similarity measurement in consideration with both graph structure and descriptive information, and a deanonymization algorithm based on the measurement. Using the proposed algorithm, we evaluated the privacy risks of several typical anonymization algorithms on social graphs with thousands of nodes from Microsoft Academic Search, LiveJournal, and the Enron email dataset, and a social graph with millions of nodes from Tencent Weibo. Our results showed that the proposed algorithm was efficient and effective to deanonymize social graphs without any initial seed mappings. Based on the experiments, we also pointed out suggestions on how to better maintain the data utility while preserving privacy.

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IJCAI Conference 2015 Conference Paper

Mobile Query Recommendation via Tensor Function Learning

  • Zhou Zhao
  • Ruihua Song
  • Xing Xie
  • Xiaofei He
  • Yueting Zhuang

With the prevalence of mobile search nowadays, the benefits of mobile query recommendation are well recognized, which provide formulated queries sticking to users’ search intent. In this paper, we introduce the problem of query recommendation on mobile devices and model the user-location-query relations with a tensor representation. Unlike previous studies based on tensor decomposition, we study this problem via tensor function learning. That is, we learn the tensor function from the side information of users, locations and queries, and then predict users’ search intent. We develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the introduced problem. We empirically evaluate our approach based on the mobile query dataset from Bing search engine in the city of Beijing, China, and show that our method can outperform several state-of-the-art approaches.

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TIST Journal 2015 Journal Article

Sensing the Pulse of Urban Refueling Behavior

  • Fuzheng Zhang
  • Nicholas Jing Yuan
  • David Wilkie
  • Yu Zheng
  • Xing Xie

Urban transportation is an important factor in energy consumption and pollution, and is of increasing concern due to its complexity and economic significance. Its importance will only increase as urbanization continues around the world. In this article, we explore drivers’ refueling behavior in urban areas. Compared to questionnaire-based methods of the past, we propose a complete data-driven system that pushes towards real-time sensing of individual refueling behavior and citywide petrol consumption. Our system provides the following: detection of individual refueling events (REs) from which refueling preference can be analyzed; estimates of gas station wait times from which recommendations can be made; an indication of overall fuel demand from which macroscale economic decisions can be made, and a spatial, temporal, and economic view of urban refueling characteristics. For individual behavior, we use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits. For time spent estimates, to solve the sparsity issue along time and stations, we propose context-aware tensor factorization (CATF), a factorization model that considers a variety of contextual factors (e.g., price, brand, and weather condition) that affect consumers’ refueling decision. For fuel demand estimates, we apply a queue model to calculate the overall visits based on the time spent inside the station. We evaluated our system on large-scale and real-world datasets, which contain 4-month trajectories of 32,476 taxicabs, 689 gas stations, and the self-reported refueling details of 8,326 online users. The results show that our system can determine REs with an accuracy of more than 90%, estimate time spent with less than 2 minutes of error, and measure overall visits in the same order of magnitude with the records in the field study.

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TIST Journal 2015 Journal Article

When Location Meets Social Multimedia

  • Rongrong Ji
  • Yue Gao
  • Wei Liu
  • Xing Xie
  • Qi Tian
  • Xuelong Li

Coming with the popularity of multimedia sharing platforms such as Facebook and Flickr, recent years have witnessed an explosive growth of geographical tags on social multimedia content. This trend enables a wide variety of emerging applications, for example, mobile location search, landmark recognition, scene reconstruction, and touristic recommendation, which range from purely research prototype to commercial systems. In this article, we give a comprehensive survey on these applications, covering recent advances in recognition and mining of geographical-aware social multimedia. We review related work in the past decade regarding to location recognition, scene summarization, tourism suggestion, 3D building modeling, mobile visual search and city navigation. At the end, we further discuss potential challenges, future topics, as well as open issues related to geo-social multimedia computing, recognition, mining, and analytics.

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TIST Journal 2014 Journal Article

Mining Check-In History for Personalized Location Naming

  • Defu Lian
  • Xing Xie

Many innovative location-based services have been established to offer users greater convenience in their everyday lives. These services usually cannot map user's physical locations into semantic names automatically. The semantic names of locations provide important context for mobile recommendations and advertisements. In this article, we proposed a novel location naming approach which can automatically provide semantic names for users given their locations and time. In particular, when a user opens a GPS device and submits a query with her physical location and time, she will be returned the most appropriate semantic name. In our approach, we drew an analogy between location naming and local search, and designed a local search framework to propose a spatiotemporal and user preference (STUP) model for location naming. STUP combined three components, user preference (UP), spatial preference (SP), and temporal preference (TP), by leveraging learning-to-rank techniques. We evaluated STUP on 466,190 check-ins of 5,805 users from Shanghai and 135,052 check-ins of 1,361 users from Beijing. The results showed that SP was most effective among three components and that UP can provide personalized semantic names, and thus it was a necessity for location naming. Although TP was not as discriminative as the others, it can still be beneficial when integrated with SP and UP. Finally, according to the experimental results, STUP outperformed the proposed baselines and returned accurate semantic names for 23.6% and 26.6% of the testing queries from Beijing and Shanghai, respectively.

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AIJ Journal 2012 Journal Article

Towards mobile intelligence: Learning from GPS history data for collaborative recommendation

  • Vincent W. Zheng
  • Yu Zheng
  • Xing Xie
  • Qiang Yang

With the increasing popularity of location-based services, we have accumulated a lot of location data on the Web. In this paper, we are interested in answering two popular location-related queries in our daily life: (1) if we want to do something such as sightseeing or dining in a large city like Beijing, where should we go? (2) If we want to visit a place such as the Birdʼs Nest in Beijing Olympic park, what can we do there? We develop a mobile recommendation system to answer these queries. In our system, we first model the usersʼ location and activity histories as a user–location–activity rating tensor. 1 1 A “tensor” is a multi-dimensional array (Symeonidis et al. , 2008 [1]; Cichocki et al. , 2009 [2]) Because each user has limited data, the resulting rating tensor is essentially very sparse. This makes our recommendation task difficult. In order to address this data sparsity problem, we propose three algorithms 2 2 This work is an extension to our previous work (Zheng et al. , 2010 [3, 4]). We propose a new model in Section 5. 3 and completely re-conduct the experiments for all our three algorithms. based on collaborative filtering. The first algorithm merges all the usersʼ data together, and uses a collective matrix factorization model to provide general recommendation (Zheng et al. , 2010 [3]). The second algorithm treats each user differently and uses a collective tensor and matrix factorization model to provide personalized recommendation (Zheng et al. , 2010 [4]). The third algorithm is a new algorithm which further improves our previous two algorithms by using a ranking-based collective tensor and matrix factorization model. Instead of trying to predict the missing entry values as accurately as possible, it focuses on directly optimizing the ranking loss w. r. t. user preferences on the locations and activities. Therefore, it is more consistent with our ultimate goal of ranking locations/activities for recommendations. For these three algorithms, we also exploit some additional information, such as user–user similarities, location features, activity–activity correlations and user–location preferences, to help the CF tasks. We extensively evaluate our algorithms using a real-world GPS dataset collected by 119 users over 2. 5 years. We show that all our three algorithms can consistently outperform the competing baselines, and our newly proposed third algorithm can also outperform our other two previous algorithms.

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TIST Journal 2011 Journal Article

Learning travel recommendations from user-generated GPS traces

  • Yu Zheng
  • Xing Xie

The advance of GPS-enabled devices allows people to record their location histories with GPS traces, which imply human behaviors and preferences related to travel. In this article, we perform two types of travel recommendations by mining multiple users' GPS traces. The first is a generic one that recommends a user with top interesting locations and travel sequences in a given geospatial region. The second is a personalized recommendation that provides an individual with locations matching her travel preferences. To achieve the first recommendation, we model multiple users' location histories with a tree-based hierarchical graph ( TBHG ). Based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based model to infer the interest level of a location and a user's travel experience (knowledge). In the personalized recommendation, we first understand the correlation between locations, and then incorporate this correlation into a collaborative filtering (CF)-based model, which predicts a user's interests in an unvisited location based on her locations histories and that of others. We evaluated our system based on a real-world GPS trace dataset collected by 107 users over a period of one year. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, we achieved a better performance in recommending travel sequences beyond baselines like rank-by-count. Regarding the personalized recommendation, our approach is more effective than the weighted Slope One algorithm with a slightly additional computation, and is more efficient than the Pearson correlation-based CF model with the similar effectiveness.

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AAAI Conference 2010 Conference Paper

Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach

  • Vincent Zheng
  • Bin Cao
  • Yu Zheng
  • Xing Xie
  • Qiang Yang

With the increasing popularity of location tracking services such as GPS, more and more mobile data are being accumulated. Based on such data, a potentially useful service is to make timely and targeted recommendations for users on places where they might be interested to go and activities that they are likely to conduct. For example, a user arriving in Beijing might wonder where to visit and what she can do around the Forbidden City. A key challenge for such recommendation problems is that the data we have on each individual user might be very limited, while to make useful and accurate recommendations, we need extensive annotated location and activity information from user trace data. In this paper, we present a new approach, known as user-centered collaborative location and activity filtering (UCLAF), to pull many users’ data together and apply collaborative filtering to find like-minded users and like-patterned activities at different locations. We model the userlocation-activity relations with a tensor representation, and propose a regularized tensor and matrix decomposition solution which can better address the sparse data problem in mobile information retrieval. We empirically evaluate UCLAF using a real-world GPS dataset collected from 164 users over 2. 5 years, and showed that our system can outperform several state-of-the-art solutions to the problem.

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