Guibing Guo

AAAI Conference 2026 Conference Paper

Interest-Shift-Aware Logical Reasoning for Efficient Long-Sequence Recommendation

• Fei Li
• Qingyun Gao
• Enneng Yang
• Jianzhe Zhao
• Guibing Guo

Logical reasoning-based recommendation methods formulate logical expressions to characterize user-item interaction patterns, incorporating regularization constraints to ensure consistency with logical rules. However, these methods face two critical challenges: (1) As sequence length increases, they cannot effectively capture the dynamic transfer of user interests across subsequences (i.e., subsequence interest drift), thereby degenerating logical expressions to single-subsequence inference. (2) The time complexity of logical reasoning and rule learning scales quadratically with the sequence length, severely constraining computational efficiency in long-sequence recommendation. To address these challenges, we propose ELECTOR, an intErest-shift-aware long-sequence Logical reasoning for EffiCienT lOng-sequence Recommendation method. Specifically, we design a Subsequence Interest Learning Module (SIL) to model cross-subsequence interest drifts in long sequences. SIL employs a local attention mechanism to extract subsequence interests effectively and a global attention mechanism to capture the correlations among subsequence interests. Subsequently, we propose an Interest-aware Logical Reasoning (ILR) mechanism that performs logical reasoning using a limited set of subsequence and short-term interests, rather than reasoning over the entire sequence, significantly reducing time complexity. Additionally, ILR employs interest logical reasoning contrastive loss to ensure the model simultaneously considers multiple interests. Experiments on four real-world datasets demonstrate that our method significantly outperforms all baselines regarding computational efficiency and recommendation accuracy, confirming its effectiveness.

AAAI Conference 2026 Conference Paper

MoFu: Scale-Aware Modulation and Fourier Fusion for Multi-Subject Video Generation

• Run Ling
• Ke Cao
• Jian Lu
• Ao Ma
• Haowei Liu
• Runze He
• Changwei Wang
• Rongtao Xu

Multi-subject video generation aims to synthesize videos from textual prompts and multiple reference images, ensuring that each subject preserves natural scale and visual fidelity. However, current methods face two challenges: scale inconsistency, where variations in subject size lead to unnatural generation, and permutation sensitivity, where the order of reference inputs causes subject distortion. In this paper, we propose MoFu, a unified framework that tackles both challenges. For scale inconsistency, we introduce Scale-Aware Modulation (SMO), an LLM-guided module that extracts implicit scale cues from the prompt and modulates features to ensure consistent subject sizes. To address permutation sensitivity, we present a simple yet effective Fourier Fusion strategy that processes the frequency information of reference features via the Fast Fourier Transform to produce a unified representation. Besides, we design a Scale-Permutation Stability Loss to jointly encourage scale-consistent and permutation-invariant generation. To further evaluate these challenges, we establish a dedicated benchmark with controlled variations in subject scale and reference permutation. Extensive experiments demonstrate that MoFu significantly outperforms existing methods in preserving natural scale, subject fidelity, and overall visual quality.

AAAI Conference 2026 Conference Paper

RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation

• Run Ling
• Wenji Wang
• Yuting Liu
• Guibing Guo
• Haowei Liu
• Jian Lu
• Quanwei Zhang
• Yexing Xu

Personalized image generation is crucial for improving the user experience, as it renders reference images into preferred ones according to user visual preferences. Although effective, existing methods face two main issues. First, existing methods treat all items in the user's historical sequence equally when extracting user preferences, overlooking the varying semantic similarities between historical items and the reference item. Disproportionately high weights for low-similarity items distort user visual preferences for the reference item. Second, existing methods heavily rely on consistency between generated and reference images to optimize generation, which leads to underfitting user preferences and hinders personalization. To address these issues, we propose Retrieval Augmented Personalized Image GenerAtion guided by Recommendation (RAGAR). Our approach uses a retrieval mechanism to assign different weights to historical items according to their similarities to the reference item, thereby extracting more refined users' visual preferences for the reference item. Then we introduce a novel rank task based on the multi-modal ranking model to optimize the personalization of the generated images instead of forcing depend on consistency. Extensive experiments and human evaluations on three real-world datasets demonstrate that RAGAR achieves significant improvements in both personalization and semantic metrics compared to five baselines.

AAAI Conference 2025 Conference Paper

Augmenting Sequential Recommendation with Balanced Relevance and Diversity

• Yizhou Dang
• Jiahui Zhang
• Yuting Liu
• Enneng Yang
• Yuliang Liang
• Guibing Guo
• Jianzhe Zhao
• Xingwei Wang

By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on augmenting the original data but rarely explore the issue of imbalanced relevance and diversity for augmented data, leading to semantic drift problems or limited performance improvements. In this paper, we propose a novel Balanced data Augmentation Plugin for Sequential Recommendation (BASRec) to generate data that balance relevance and diversity. BASRec consists of two modules: Single-sequence Augmentation and Cross-sequence Augmentation. The former leverages the randomness of the heuristic operators to generate diverse sequences for a single user, after which the diverse and the original sequences are fused at the representation level to obtain relevance. Further, we devise a reweighting strategy to enable the model to learn the preferences based on the two properties adaptively. The Cross-sequence Augmentation performs nonlinear mixing between different sequence representations from two directions. It produces virtual sequence representations that are diverse enough but retain the vital semantics of the original sequences. These two modules enhance the model to discover fine-grained preferences knowledge from single-user and cross-user perspectives. Extensive experiments verify the effectiveness of BASRec. The average improvement is up to 72.0% on GRU4Rec, 33.8% on SASRec, and 68.5% on FMLP-Rec. We demonstrate that BASRec generates data with a better balance between relevance and diversity than existing methods.

NeurIPS Conference 2025 Conference Paper

Continual Model Merging without Data: Dual Projections for Balancing Stability and Plasticity

• Enneng Yang
• Anke Tang
• Li Shen
• Guibing Guo
• Xingwei Wang
• Xiaochun Cao
• Jie Zhang

Model merging integrates multiple expert models with diverse capabilities into a unified framework, facilitating collaborative learning. However, most existing methods assume simultaneous access to all models, which is often impractical in real-world scenarios where models are received sequentially. While some studies have investigated continual model merging (CMM)--which involves sequentially merging multiple models--the challenge of balancing prior knowledge (stability) and incorporating new tasks (plasticity) remains unresolved. This paper, for the first time, formally defines the stability and plasticity of CMM from the perspective of orthogonal projection. Subsequently, we analyze the relationships among the spaces spanned by task data, historical gradients, and accumulated gradients. Building on this, we propose a data-free \textbf{D}ual \textbf{O}rthogonal \textbf{P}rojection (DOP) method, which eliminates data dependence and mitigates interference between the merged model and models for old and new tasks by projecting their parameter differences onto their respective approximate data spaces. Finally, to solve potential conflicts between stability and plasticity, we reformulate DOP as a multi-objective optimization problem and employ a multi-gradient descent algorithm to obtain a Pareto-optimal solution. Extensive experiments across multiple architectures and task configurations validate that our approach significantly outperforms state-of-the-art CMM methods.

AAAI Conference 2025 Conference Paper

CoRA: Collaborative Information Perception by Large Language Model’s Weights for Recommendation

• Yuting Liu
• Jinghao Zhang
• Yizhou Dang
• Yuliang Liang
• Qiang Liu
• Guibing Guo
• Jianzhe Zhao
• Xingwei Wang

Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating collaborative features with text tokens into a unified sequence input and then fine-tuning to align these features with LLM's input space. Although effective, in this work, we identify two limitations when adapting LLMs to recommendation tasks, which hinder the integration of general knowledge and collaborative information, resulting in sub-optimal recommendation performance. (1) Fine-tuning LLM with recommendation data can undermine its inherent world knowledge and fundamental competencies, which are crucial for interpreting and inferring recommendation text. (2) Incorporating collaborative features into textual prompts disrupts the semantics of the original prompts, preventing LLM from generating appropriate outputs. In this paper, we propose a new paradigm, Collaborative LoRA (CoRA), with a collaborative query generator. Rather than input space alignment, this method aligns collaborative information with LLM's parameter space, representing them as incremental weights to update LLM's output. This way, LLM perceives collaborative information without altering its general knowledge and text inference capabilities. Specifically, we employ a collaborative filtering model to extract user and item embeddings and inject them into a set number of learnable queries. We then convert collaborative queries into collaborative weights with low-rank properties and merge the collaborative weights into LLM's weights, enabling LLM to perceive the collaborative signals and generate personalized recommendations without fine-tuning or extra collaborative tokens in prompts. Extensive experiments confirm that CoRA effectively integrates collaborative information into LLM, enhancing recommendation performance.

AAAI Conference 2025 Conference Paper

EPT: Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion

• Pengxiang Lan
• Enneng Yang
• Yuting Liu
• Guibing Guo
• Jianzhe Zhao
• Xingwei Wang

Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, whereby downstream tasks can be well adapted by merely learning the embeddings of prompt tokens. Nevertheless, existing methods still suffer from two challenges: (i) they are hard to balance accuracy and efficiency. A longer (shorter) soft prompt generally leads to a better (worse) accuracy but at the cost of more (less) training time. (ii) The performance may not be consistent when adapting to different downstream tasks. We attribute it to the same embedding space but responsible for different requirements of downstream tasks. To address these issues, we propose an Efficient Prompt Tuning method (EPT) by multi-space projection and prompt fusion. Specifically, it decomposes a given soft prompt into a shorter prompt and two low-rank matrices, significantly reducing the training time. Accuracy is also enhanced by leveraging low-rank matrices and the short prompt as additional knowledge sources to enrich the semantics of the original short prompt. In addition, we project the soft prompt into multiple subspaces to improve the performance consistency, and then adaptively learn the combination weights of different spaces through a gating network. Experiments on 13 natural language processing downstream tasks show that our method significantly and consistently outperforms 11 comparison methods with the relative percentage of improvements up to 12.9%, and training time decreased by 14%.

AAAI Conference 2025 Conference Paper

Multiple Purchase Chains with Negative Transfer Elimination for Multi-Behavior Recommendation

• Shuwei Gong
• Yuting Liu
• Yizhou Dang
• Guibing Guo
• Jianzhe Zhao
• Xingwei Wang

Multi-behavior recommendation exploits auxiliary behaviors (e.g., view, cart) to help predict users' potential target behavior (e.g., purchase) on a given item. However, existing works suffer from two issues: (1) They generally consider only a single chain from auxiliary behaviors to the target behavior, referred to as a purchase chain (e.g., view -> cart -> purchase), ignoring other valuable purchase chains (e.g., view ->purchase) that are beneficial for recommendation performance. (2) Most studies presume that interacted items in auxiliary behaviors are good for recommendations, and pay little attention to the negative transfer problem. That is, some auxiliary behaviors may negatively transfer the influence to the modeling of target ones (e.g., items viewed but not purchased). To alleviate these issues, we propose a novel Multiple Purchase Chains (MPC) model with negative transfer elimination for multi-behavior recommendation. Specifically, we construct multiple purchase chains from auxiliary to target behaviors according to users' historical interactions, while the representations of a previous behavior will be fed to initialize the next behavior on the chain. Then, we construct a negative graph for the latter behavior and learn the negative representations of users and items which will be filtered out to eliminate negative transfer. Experimental results on two real datasets outperform the best baseline by 40.97% and 47.26% on average in terms of Recall@10 and NDCG@10 respectively, demonstrating the effectiveness of our method.

ICLR Conference 2024 Conference Paper

AdaMerging: Adaptive Model Merging for Multi-Task Learning

• Enneng Yang
• Zhenyi Wang 0001
• Li Shen 0008
• Shiwei Liu 0003
• Guibing Guo
• Xingwei Wang 0001
• Dacheng Tao

Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously. A recent development known as task arithmetic has revealed that several models, each fine-tuned for distinct tasks, can be directly merged into a single model to execute MTL without necessitating a retraining process using the initial training data. Nevertheless, this direct addition of models often leads to a significant deterioration in the overall performance of the merged model. This decline occurs due to potential conflicts and intricate correlations among the multiple tasks. Consequently, the challenge emerges of how to merge pre-trained models more effectively without using their original training data. This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging). This approach aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data. Specifically, our AdaMerging method operates as an automatic, unsupervised task arithmetic scheme. It leverages entropy minimization on unlabeled test samples from the multi-task setup as a surrogate objective function to iteratively refine the merging coefficients of the multiple models. Our experimental findings across eight tasks demonstrate the efficacy of the AdaMerging scheme we put forth. Compared to the current state-of-the-art (SOTA) task arithmetic merging scheme, AdaMerging showcases a remarkable 11\% improvement in performance. Notably, AdaMerging also exhibits superior generalization capabilities when applied to unseen downstream tasks. Furthermore, it displays a significantly enhanced robustness to data distribution shifts that may occur during the testing phase.

ICML Conference 2024 Conference Paper

Representation Surgery for Multi-Task Model Merging

• Enneng Yang
• Li Shen 0008
• Zhenyi Wang 0001
• Guibing Guo
• Xiaojun Chen 0006
• Xingwei Wang 0001
• Dacheng Tao

Multi-task learning (MTL) compresses the information from multiple tasks into a unified backbone to improve computational efficiency and generalization. Recent work directly merges multiple independently trained models to perform MTL instead of collecting their raw data for joint training, greatly expanding the application scenarios of MTL. However, by visualizing the representation distribution of existing model merging schemes, we find that the merged model often suffers from the dilemma of representation bias. That is, there is a significant discrepancy in the representation distribution between the merged and individual models, resulting in poor performance of merged MTL. In this paper, we propose a representation surgery solution called “Surgery" to reduce representation bias in the merged model. Specifically, Surgery is a lightweight task-specific plugin that takes the representation of the merged model as input and attempts to output the biases contained in the representation from the merged model. We then designed an unsupervised optimization objective that updates the Surgery plugin by minimizing the distance between the merged model’s representation and the individual model’s representation. Extensive experiments demonstrate significant MTL performance improvements when our Surgery plugin is applied to state-of-the-art (SOTA) model merging schemes.

AAAI Conference 2023 Conference Paper

AdaTask: A Task-Aware Adaptive Learning Rate Approach to Multi-Task Learning

• Enneng Yang
• Junwei Pan
• Ximei Wang
• Haibin Yu
• Li Shen
• Xihua Chen
• Lei Xiao
• Jie Jiang

Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems. Even though many approaches have been proposed, how well these approaches balance different tasks on each parameter still remains unclear. In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter. Specifically, we compute the total updates by the exponentially decaying Average of the squared Updates (AU) on a parameter from the corresponding task. Based on this novel metric, we observe that many parameters in existing MTL methods, especially those in the higher shared layers, are still dominated by one or several tasks. The dominance of AU is mainly due to the dominance of accumulative gradients from one or several tasks. Motivated by this, we propose a Task-wise Adaptive learning rate approach, AdaTask in short, to separate the accumulative gradients and hence the learning rate of each task for each parameter in adaptive learning rate approaches (e.g., AdaGrad, RMSProp, and Adam). Comprehensive experiments on computer vision and recommender system MTL datasets demonstrate that AdaTask significantly improves the performance of dominated tasks, resulting SOTA average task-wise performance. Analysis on both synthetic and real-world datasets shows AdaTask balance parameters in every shared layer well.

NeurIPS Conference 2023 Conference Paper

An Efficient Dataset Condensation Plugin and Its Application to Continual Learning

• Enneng Yang
• Li Shen
• Zhenyi Wang
• Tongliang Liu
• Guibing Guo

Dataset condensation (DC) distills a large real-world dataset into a small synthetic dataset, with the goal of training a network from scratch on the latter that performs similarly to the former. State-of-the-art (SOTA) DC methods have achieved satisfactory results through techniques such as accuracy, gradient, training trajectory, or distribution matching. However, these works all perform matching in the high-dimension pixel spaces, ignoring that natural images are usually locally connected and have lower intrinsic dimensions, resulting in low condensation efficiency. In this work, we propose a simple-yet-efficient dataset condensation plugin that matches the raw and synthetic datasets in a low-dimensional manifold. Specifically, our plugin condenses raw images into two low-rank matrices instead of parameterized image matrices. Our plugin can be easily incorporated into existing DC methods, thereby containing richer raw dataset information at limited storage costs to improve the downstream applications' performance. We verify on multiple public datasets that when the proposed plugin is combined with SOTA DC methods, the performance of the network trained on synthetic data is significantly improved compared to traditional DC methods. Moreover, when applying the DC methods as a plugin to continual learning tasks, we observed that our approach effectively mitigates catastrophic forgetting of old tasks under limited memory buffer constraints and avoids the problem of raw data privacy leakage.

IJCAI Conference 2023 Conference Paper

Basket Representation Learning by Hypergraph Convolution on Repeated Items for Next-basket Recommendation

• Yalin Yu
• Enneng Yang
• Guibing Guo
• Linying Jiang
• Xingwei Wang

Basket representation plays an important role in the task of next-basket recommendation. However, existing methods generally adopts pooling operations to learn a basket's representation, from which two critical issues can be identified. First, they treat a basket as a set of items independent and identically distributed. We find that items occurring in the same basket have much higher correlations than those randomly selected by conducting data analysis on a real dataset. Second, although some works have recognized the importance of items repeatedly purchased in multiple baskets, they ignore the correlations among the repeated items in a same basket, whose importance is shown by our data analysis. In this paper, we propose a novel Basket Representation Learning (BRL) model by leveraging the correlations among intra-basket items. Specifically, we first connect all the items (in a basket) as a hyperedge, where the correlations among different items can be well exploited by hypergraph convolution operations. Meanwhile, we also connect all the repeated items in the same basket as a hyperedge, whereby their correlations can be further strengthened. We generate a negative (positive) view of the basket by data augmentation on repeated (non-repeated) items, and apply contrastive learning to force more agreements on repeated items. Finally, experimental results on three real datasets show that our approach performs better than eight baselines in ranking accuracy.

AAAI Conference 2023 Conference Paper

Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation

• Yizhou Dang
• Enneng Yang
• Guibing Guo
• Linying Jiang
• Xingwei Wang
• Xiaoxiao Xu
• Qinghui Sun
• Hong Liu

Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of preference drift. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 9 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec.

IS Journal 2021 Journal Article

Adversarial Path Sampling for Recommender Systems

• Rui Ding
• Bowei Chen
• Guibing Guo
• Xiaochun Yang

Generative adversarial networks (GANs) have achieved a big success in collaborative filtering (CF). However, existing GAN-based methods in CF still suffer from the high-sparsity and cold-start problems; in addition, they also undergo the issues of excessive space complexity or inadequate training. In this article, we propose path2rec a novel adversarial path-based recommendation model to address these limitations of existing GAN-based methods in recommendation task by naturally incorporating auxiliary information (e. g. , social networks and item attributes). It is composed of two modules, 1) pathGAN and 2) path2vec. In pathGAN, we consider both explicit and implicit friends, as well as item attributes by regarding them as the source of graph construction. Then, we propose a smart walk strategy to automatically generate an optimizing path, which can effectively learn the semantic distribution of users and items. In path2vec, to fully exploit context features of the generated path, we use the Continuous Bag of Words (CBOW) model to fine-tune nodes representations learned by pathGAN. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of the proposed path2rec by applying it into top- n item recommendation, which reaches better performance than other counterparts.

IS Journal 2021 Journal Article

VSE-fs: Fast Full-Sample Visual Semantic Embedding

• Songlin Zhai
• Guibing Guo
• Fajie Yuan
• Yuan Liu
• Xingwei Wang

The visual semantic embedding (VSE) aims to construct a joint embedding space between visual features and semantic information, whereby classes can be well retrieved for a given image. However, VSE faces the computational challenge due to the large scale image-class data and the constrained system processing power. To speed up model training, many researchers resort to different sampling strategies by involving only a small portion of the classes at each training step. However, these methods are greatly biased especially when the sampling distribution deviates from the true data distribution. In order to retain VSE models fidelity, we adopt the regular full-sample in our algorithm. We also devise two separate optimization strategies to reduce time complexity, and derive more effective updating rules. The experimental results on four real datasets demonstrate that our approach not only converges much faster than the state-of-the-art sampling models, but also generates more accurate class retrieval.

AAAI Conference 2020 Conference Paper

Leveraging Title-Abstract Attentive Semantics for Paper Recommendation

• Guibing Guo
• Bowei Chen
• Xiaoyan Zhang
• Zhirong Liu
• Zhenhua Dong
• Xiuqiang He

Paper recommendation is a research topic to provide users with personalized papers of interest. However, most existing approaches equally treat title and abstract as the input to learn the representation of a paper, ignoring their semantic relationship. In this paper, we regard the abstract as a sequence of sentences, and propose a two-level attentive neural network to capture: (1) the ability of each word within a sentence to reflect if it is semantically close to the words within the title. (2) the extent of each sentence in the abstract relative to the title, which is often a good summarization of the abstract document. Specifically, we propose a Long-Short Term Memory (LSTM) network with attention to learn the representation of sentences, and integrate a Gated Recurrent Unit (GRU) network with a memory network to learn the long-term sequential sentence patterns of interacted papers for both user and item (paper) modeling. We conduct extensive experiments on two real datasets, and show that our approach outperforms other state-of-the-art approaches in terms of accuracy.

IJCAI Conference 2019 Conference Paper

Discrete Trust-aware Matrix Factorization for Fast Recommendation

• Guibing Guo
• Enneng Yang
• Li Shen
• Xiaochun Yang
• Xiaodong He

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

IJCAI Conference 2019 Conference Paper

Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation

• Guibing Guo
• Shichang Ouyang
• Xiaodong He
• Fajie Yuan
• Xiaohua Liu

Sequential recommendation systems have become a research hotpot recently to suggest users with the next item of interest (to interact with). However, existing approaches suffer from two limitations: (1) The representation of an item is relatively static and fixed for all users. We argue that even a same item should be represented distinctively with respect to different users and time steps. (2) The generation of a prediction for a user over an item is computed in a single scale (e. g. , by their inner product), ignoring the nature of multi-scale user preferences. To resolve these issues, in this paper we propose two enhancing building blocks for sequential recommendation. Specifically, we devise a Dynamic Item Block (DIB) to learn dynamic item representation by aggregating the embeddings of those who rated the same item before that time step. Then, we come up with a Prediction Enhancing Block (PEB) to project user representation into multiple scales, based on which many predictions can be made and attentively aggregated for enhanced learning. Each prediction is generated by a softmax over a sampled itemset rather than the whole item space for efficiency. We conduct a series of experiments on four real datasets, and show that even a basic model can be greatly enhanced with the involvement of DIB and PEB in terms of ranking accuracy. The code and datasets can be obtained from https: //github. com/ouououououou/DIB-PEB-Sequential-RS

IJCAI Conference 2018 Conference Paper

Approximating Word Ranking and Negative Sampling for Word Embedding

• Guibing Guo
• Shichang Ouyang
• Fajie Yuan
• Xingwei Wang

CBOW (Continuous Bag-Of-Words) is one of the most commonly used techniques to generate word embeddings in various NLP tasks. However, it fails to reach the optimal performance due to uniform involvements of positive words and a simple sampling distribution of negative words. To resolve these issues, we propose OptRank to optimize word ranking and approximate negative sampling for bettering word embedding. Specifically, we first formalize word embedding as a ranking problem. Then, we weigh the positive words by their ranks such that highly ranked words have more importance, and adopt a dynamic sampling strategy to select informative negative words. In addition, an approximation method is designed to efficiently compute word ranks. Empirical experiments show that OptRank consistently outperforms its counterparts on a benchmark dataset with different sampling scales, especially when the sampled subset is small. The code and datasets can be obtained from https: //github. com/ouououououou/OptRank.

UAI Conference 2018 Conference Paper

f BGD: Learning Embeddings From Positive Unlabeled Data with BGD

• Fajie Yuan
• Xin Xin 0003
• Xiangnan He 0001
• Guibing Guo
• Weinan Zhang 0001
• Tat-Seng Chua
• Joemon M. Jose

Learning sparse features from only positive and unlabeled (PU) data is a fundamental task for problems of several domains, such as natural language processing (NLP), computer vision (CV), information retrieval (IR). Considering the numerous amount of unlabeled data, most prevalent methods rely on negative sampling (NS) to increase computational efficiency. However, sampling a fraction of unlabeled data as negative for training may ignore other important examples, and thus lead to non-optimal prediction performance. To address this, we present a fast and generic batch gradient descent optimizer (fBGD ) to learn from all training examples without sampling. By leveraging sparsity in PU data, we accelerate fBGD by several magnitudes, making its time complexity the same level as the NSbased stochastic gradient descent method. Meanwhile, we observe that the standard batch gradient method suffers from gradient instability issues due to the sparsity property. Driven by a theoretical analysis for this potential cause, an intuitive solution arises naturally. To verify its efficacy, we perform experiments on multiple tasks with PU data across domains, and show that fBGD consistently outperforms NS-based models on all tasks with comparable efficiency.

AAAI Conference 2018 Conference Paper

VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling

• Guibing Guo
• Songlin Zhai
• Fajie Yuan
• Yuan Liu
• Xingwei Wang

Jointing visual-semantic embeddings (VSE) have become a research hotpot for the task of image annotation, which suffers from the issue of semantic gap, i. e. , the gap between images’ visual features (low-level) and labels’ semantic features (high-level). This issue will be even more challenging if visual features cannot be retrieved from images, that is, when images are only denoted by numerical IDs as given in some real datasets. The typical way of existing VSE methods is to perform a uniform sampling method for negative examples that violate the ranking order against positive examples, which requires a time-consuming search in the whole label space. In this paper, we propose a fast adaptive negative sampler that can work well in the settings of no figure pixels available. Our sampling strategy is to choose the negative examples that are most likely to meet the requirements of violation according to the latent factors of images. In this way, our approach can linearly scale up to large datasets. The experiments demonstrate that our approach converges 5. 02x faster than the state-of-the-art approaches on OpenImages, 2. 5x on IAPR-TCI2 and 2. 06x on NUS-WIDE datasets, as well as better ranking accuracy across datasets.

AAAI Conference 2015 Conference Paper

TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings

• Guibing Guo
• Jie Zhang
• Neil Yorke-Smith

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

IJCAI Conference 2013 Conference Paper

A Novel Bayesian Similarity Measure for Recommender Systems

• Guibing Guo
• Jie Zhang
• Neil Yorke-Smith

Collaborative filtering, a widely-used user-centric recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is usually calculated by cosine similarity or Pearson correlation coefficient. However, both of them consider only the direction of rating vectors, and suffer from a range of drawbacks. To solve these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. Further, our principled method reduces correlation due to chance. Experimental results on six real-world data sets show that our method achieves superior accuracy.

IJCAI Conference 2013 Conference Paper

Improving the Performance of Recommender Systems by Alleviating the Data Sparsity and Cold Start Problems

• Guibing Guo