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Chuan Qin

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13 papers

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AAAI Conference 2026 Conference Paper

Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models

Yongwen Ren
Chao Wang
Peng Du
Chuan Qin
Dazhong Shen
Hui Xiong

Recent advances in pretrained language models (PLMs) have significantly improved conversational recommender systems (CRS), enabling more fluent and context-aware interactions. To further enhance accuracy and mitigate hallucination, many methods integrate PLMs with knowledge graphs (KGs), but face key challenges: failing to fully exploit PLM reasoning over graph relationships, indiscriminately incorporating retrieved knowledge without context filtering, and neglecting collaborative preferences in multi-turn dialogues. To this end, we propose PCRS-TKA, a prompt-based framework employing retrieval-augmented generation to integrate PLMs with KGs. PCRS-TKA constructs dialogue-specific knowledge trees from KGs and serializes them into texts, enabling structure-aware reasoning while capturing rich entity semantics. Our approach selectively filters context-relevant knowledge and explicitly models collaborative preferences using specialized supervision signals. A semantic alignment module harmonizes heterogeneous inputs, reducing noise and enhancing accuracy. Extensive experiments demonstrate that PCRS-TKA consistently outperforms all baselines in both recommendation and conversational quality.

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EAAI Journal 2026 Journal Article

Image manipulation localization using multi-noise fusion and learnable compression artifacts

Qiang Wu
Weimin Wei
Fengyong Li
Chuan Qin

Image manipulation localization aims to accurately identify and locate the tampered areas in digital images. Existing deep learning-based methods usually overly rely on single-modal features and cannot effectively fuse multi-source feature information, resulting in poor identification performance and robustness in high-resolution scenarios. To address the aforementioned problem, we propose a new multi-stream fusion image manipulation localization architecture. Firstly, an efficient noise-stream fusion module is designed to integrate multi-source noise features, which can effectively filter out feature redundancy to achieve complementary advantages of noise features. Subsequently, we design a compression artifact recognition module by comprehensively utilizing the frequency feature after Discrete Cosine Transform (DCT) transformation, which can significantly improve the model’s resistance to post-processing by suppressing semantic information in the image. Furthermore, we introduce a cross-modal fusion architecture (CMX) to integrate noise and frequency information under different modes. This architecture allows for mutual correction between different modalities, and thus achieves the effective fusion of multi-modal information. A series of extensive experiments verify that the proposed scheme achieves effective localization capability for multi-scale manipulation regions over different image sets, and outperform most of state-of-the-art schemes in terms of detection accuracy, generalization and robustness.

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NeurIPS Conference 2025 Conference Paper

FACE: A General Framework for Mapping Collaborative Filtering Embeddings into LLM Tokens

Chao Wang
Yixin Song
Jinhui Ye
Chuan Qin
Dazhong Shen
Lingfeng Liu
Xiang Wang
Yanyong Zhang

Recently, large language models (LLMs) have been explored for integration with collaborative filtering (CF)-based recommendation systems, which are crucial for personalizing user experiences. However, a key challenge is that LLMs struggle to interpret the latent, non-semantic embeddings produced by CF approaches, limiting recommendation effectiveness and further applications. To address this, we propose FACE, a general interpretable framework that maps CF embeddings into pre-trained LLM tokens. Specifically, we introduce a disentangled projection module to decompose CF embeddings into concept-specific vectors, followed by a quantized autoencoder to convert continuous embeddings into LLM tokens (descriptors). Then, we design a contrastive alignment objective to ensure that the tokens align with corresponding textual signals. Hence, the model-agnostic FACE framework achieves semantic alignment without fine-tuning LLMs and enhances recommendation performance by leveraging their pre-trained capabilities. Empirical results on three real-world recommendation datasets demonstrate performance improvements in benchmark models, with interpretability studies confirming the interpretability of the descriptors. Code is available in \url{https: //github. com/YixinRoll/FACE}.

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IJCAI Conference 2024 Conference Paper

DGCD: An Adaptive Denoising GNN for Group-level Cognitive Diagnosis

Haiping Ma
Siyu Song
Chuan Qin
Xiaoshan Yu
Limiao Zhang
Xingyi Zhang
Hengshu Zhu

Group-level cognitive diagnosis, pivotal in intelligent education, aims to effectively assess group-level knowledge proficiency by modeling the learning behaviors of individuals within the group. Existing methods typically conceptualize the group as an abstract entity or aggregate the knowledge levels of all members to represent the group’s overall ability. However, these methods neglect the high-order connectivity among groups, students, and exercises within the context of group learning activities, along with the noise present in their interactions, resulting in less robust and suboptimal diagnosis performance. To this end, in this paper, we propose DGCD, an adaptive Denoising graph neural network for realizing effective Group-level Cognitive Diagnosis. Specifically, we first construct a group-student-exercise (GSE) graph to explicitly model higher-order connectivity among groups, students, and exercises, contributing to the acquisition of informative representations. Then, we carefully design an adaptive denoising module, integrated into the graph neural network, to model the reliability distribution of student-exercise edges for mining purer interaction features. In particular, edges of lower reliability are more prone to exclusion, thereby reducing the impact of noisy interactions. Furthermore, recognizing the relational imbalance in the GSE graph, which could potentially introduce bias during message passing, we propose an entropy-weighted balance module to mitigate such bias. Finally, extensive experiments conducted on four real-world educational datasets clearly demonstrate the effectiveness of our proposed DGCD model. The code is available at https: //github. com/BIMK/Intelligent-Education/tree/main/DGCD.

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NeurIPS Conference 2024 Conference Paper

Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking

Xi Chen
Chuan Qin
Chuyu Fang
Chao Wang
Chen Zhu
Fuzhen Zhuang
Hengshu Zhu
Hui Xiong

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https: //github. com/Job-SDF/benchmark.

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IJCAI Conference 2024 Conference Paper

Pre-DyGAE: Pre-training Enhanced Dynamic Graph Autoencoder for Occupational Skill Demand Forecasting

Xi Chen
Chuan Qin
Zhigaoyuan Wang
Yihang Cheng
Chao Wang
Hengshu Zhu
Hui Xiong

Occupational skill demand (OSD) forecasting seeks to predict dynamic skill demand specific to occupations, beneficial for employees and employers to grasp occupational nature and maintain a competitive edge in the rapidly evolving labor market. Although recent research has proposed data-driven techniques for forecasting skill demand, the focus has remained predominantly on overall trends rather than occupational granularity. In this paper, we propose a novel Pre-training Enhanced Dynamic Graph Autoencoder (Pre-DyGAE), forecasting skill demand from an occupational perspective. Specifically, we aggregate job descriptions (JDs) by occupation and segment them into several timestamps. Subsequently, in the initial timestamps, we pre-train a graph autoencoder (GAE), consisting of a semantically-aware cross-attention enhanced uncertainty-aware encoder and decoders for link prediction and edge regression to achieve graph reconstruction. In particular, we utilize contrastive learning on skill cooccurrence clusters to solve the data sparsity and a unified Tweedie and ranking loss for predicting the imbalanced distribution. Afterward, we incorporate an adaptive temporal encoding unit and a temporal shift module into GAE to achieve a dynamic GAE (DyGAE). Furthermore, we fine-tune the DyGAE with a two-stage optimization strategy and infer future representations. Extensive experiments on four real-world datasets validate the effectiveness of Pre-DyGAE compared with state-of-the-art baselines.

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NeurIPS Conference 2021 Conference Paper

Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

Ying Sun
Hengshu Zhu
Chuan Qin
Fuzhen Zhuang
Qing He
Hui Xiong

Neural network based deep learning techniques have shown great success for numerous applications. While it is expected to understand their intrinsic decision-making processes, these deep neural networks often work in a black-box way. To this end, in this paper, we aim to discern the decision-making processes of neural networks through a hierarchical voting strategy by developing an explainable deep learning model, namely Voting Transformation-based Explainable Neural Network (VOTEN). Specifically, instead of relying on massive feature combinations, VOTEN creatively models expressive single-valued voting functions between explicitly modeled latent concepts to achieve high fitting ability. Along this line, we first theoretically analyze the major components of VOTEN and prove the relationship and advantages of VOTEN compared with Multi-Layer Perceptron (MLP), the basic structure of deep neural networks. Moreover, we design efficient algorithms to improve the model usability by explicitly showing the decision processes of VOTEN. Finally, extensive experiments on multiple real-world datasets clearly validate the performances and explainability of VOTEN.

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IJCAI Conference 2021 Conference Paper

Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness

Dazhong Shen
Chuan Qin
Chao Wang
Hengshu Zhu
Enhong Chen
Hui Xiong

As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference. However, when the decoder network is sufficiently expressive, VAE may lead to posterior collapse; that is, uninformative latent representations may be learned. To this end, in this paper, we propose an alternative model, DU-VAE, for learning a more Diverse and less Uncertain latent space, and thus the representation can be learned in a meaningful and compact manner. Specifically, we first theoretically demonstrate that it will result in better latent space with high diversity and low uncertainty awareness by controlling the distribution of posterior’s parameters across the whole data accordingly. Then, without the introduction of new loss terms or modifying training strategies, we propose to exploit Dropout on the variances and Batch-Normalization on the means simultaneously to regularize their distributions implicitly. Furthermore, to evaluate the generalization effect, we also exploit DU-VAE for inverse autoregressive flow based-VAE (VAE-IAF) empirically. Finally, extensive experiments on three benchmark datasets clearly show that our approach can outperform state-of-the-art baselines on both likelihood estimation and underlying classification tasks.

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NeurIPS Conference 2021 Conference Paper

Topic Modeling Revisited: A Document Graph-based Neural Network Perspective

Dazhong Shen
Chuan Qin
Chao Wang
Zheng Dong
Hengshu Zhu
Hui Xiong

Most topic modeling approaches are based on the bag-of-words assumption, where each word is required to be conditionally independent in the same document. As a result, both of the generative story and the topic formulation have totally ignored the semantic dependency among words, which is important for improving the semantic comprehension and model interpretability. To this end, in this paper, we revisit the task of topic modeling by transforming each document into a directed graph with word dependency as edges between word nodes, and develop a novel approach, namely Graph Neural Topic Model (GNTM). Specifically, in GNTM, a well-defined probabilistic generative story is designed to model both the graph structure and word sets with multinomial distributions on the vocabulary and word dependency edge set as the topics. Meanwhile, a Neural Variational Inference (NVI) approach is proposed to learn our model with graph neural networks to encode the document graphs. Besides, we theoretically demonstrate that Latent Dirichlet Allocation (LDA) can be derived from GNTM as a special case with similar objective functions. Finally, extensive experiments on four benchmark datasets have clearly demonstrated the effectiveness and interpretability of GNTM compared with state-of-the-art baselines.

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NeurIPS Conference 2020 Conference Paper

GreedyFool: Distortion-Aware Sparse Adversarial Attack

Xiaoyi Dong
DongDong Chen
Jianmin Bao
Chuan Qin
Lu Yuan
Weiming Zhang
Nenghai Yu
Dong Chen

Modern deep neural networks(DNNs) are vulnerable to adversarial samples. Sparse adversarial samples are a special branch of adversarial samples that can fool the target model by only perturbing a few pixels. The existence of the sparse adversarial attack points out that DNNs are much more vulnerable than people believed, which is also a new aspect for analyzing DNNs. However, current sparse adversarial attack methods still have some shortcomings on both sparsity and invisibility. In this paper, we propose a novel two-stage distortion-aware greedy-based method dubbed as ''GreedyFool". Specifically, it first selects the most effective candidate positions to modify by considering both the gradient(for adversary) and the distortion map(for invisibility), then drops some less important points in the reduce stage. Experiments demonstrate that compared with the start-of-the-art method, we only need to modify 3 times fewer pixels under the same sparse perturbation setting. For target attack, the success rate of our method is 9. 96% higher than the start-of-the-art method under the same pixel budget.

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AAAI Conference 2020 Conference Paper

SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

Chao Wang
Hengshu Zhu
Chen Zhu
Chuan Qin
Hui Xiong

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reﬂect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the wellknown pairwise and listwise approaches have still been limited by various challenges. Speciﬁcally, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efﬁciently accommodate “ties” due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. Speciﬁcally, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.

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AAAI Conference 2018 Conference Paper

How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks

Linli Xu
Liang Jiang
Chuan Qin
Zhe Wang
Dongfang Du

With the recent advances of neural models and natural language processing, automatic generation of classical Chinese poetry has drawn signiﬁcant attention due to its artistic and cultural value. Previous works mainly focus on generating poetry given keywords or other text information, while visual inspirations for poetry have been rarely explored. Generating poetry from images is much more challenging than generating poetry from text, since images contain very rich visual information which cannot be described completely using several keywords, and a good poem should convey the image accurately. In this paper, we propose a memory based neural model which exploits images to generate poems. Specifically, an Encoder-Decoder model with a topic memory network is proposed to generate classical Chinese poetry from images. To the best of our knowledge, this is the ﬁrst work attempting to generate classical Chinese poetry from images with neural networks. A comprehensive experimental investigation with both human evaluation and quantitative analysis demonstrates that the proposed model can generate poems which convey images accurately.

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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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