Chaokun Wang

AAAI Conference 2025 Conference Paper

Learning Multiple User Distributions for Recommendation via Guided Conditional Diffusion

• Cheng Wu
• Liang Su
• Chaokun Wang
• Shaoyun Shi
• Ziqian Zhang
• Ziyang Liu
• Wang Peng
• Wenjin Wu

Recommender systems are increasingly prevalent to provide personalized suggestions and enhance user satisfaction. Typical recommendation models encode users and items as embeddings, and generate recommendations by assessing the similarity between these embeddings. Despite their effectiveness, these embedding-based models struggle with modeling user uncertainty and capturing diverse user interests using a single fixed user embedding. Recent studies have begun to explore a user-distribution paradigm to learn distributions for users. However, this approach employs a single distribution per user, which fails to effectively delineate semantic boundaries, resulting in sub-optimal recommendations. To this end, we propose GCDR, a Guided Conditional Diffusion Recommender model, to learn multiple distributions for each user in this paper. Specifically, GCDR addresses two major challenges: 1) learning disentangled distributions, and 2) learning personalized distributions. GCDR captures inter-user and intra-user distribution properties through conditional and guided diffusion, respectively. It maintains user-specific embeddings to encode long-term interests for conditional diffusion, while for guided diffusion, it incorporates short-term interests encoded from recent interactions with category preferences. To align the diffusion model with the recommendation task, we train GCDR with three loss functions, included the user loss, the recommendation loss and the diffusion loss. Extensive experiments on four real-world datasets show that GCDR is able to learn effective user distributions and is superior to thirteen state-of-the-art baseline methods.

NeurIPS Conference 2025 Conference Paper

Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation

• Leqi Zheng
• Chaokun Wang
• Zixin Song
• Cheng Wu
• Shannan Yan
• Jiajun Zhang
• Ziyang Liu

Traditional recommender systems have relied heavily on positive feedback for learning user preferences, while the abundance of negative feedback in real-world scenarios remains underutilized. To address this limitation, recent years have witnessed increasing attention on leveraging negative feedback in recommender systems to enhance recommendation performance. However, existing methods face three major challenges: limited model compatibility, ineffective information exchange, and computational inefficiency. To overcome these challenges, we propose a model-agnostic Signed Dual-Channel Graph Contrastive Learning (SDCGCL) framework that can be seamlessly integrated with existing graph contrastive learning methods. The framework features three key components: (1) a Dual-Channel Graph Embedding that separately processes positive and negative graphs, (2) a Cross-Channel Distribution Calibration mechanism to maintain structural consistency, and (3) an Adaptive Prediction Strategy that effectively combines signals from both channels. Building upon this framework, we further propose a Dual-channel Feedback Fusion (DualFuse) model and develop a two-stage optimization strategy to ensure efficient training. Extensive experiments on four public datasets demonstrate that our approach consistently outperforms state-of-the-art baselines by substantial margins while exhibiting minimal computational complexity. Our source code and data are released at \url{https: //github. com/LQgdwind/nips25-sdcgcl}.

ICML Conference 2025 Conference Paper

What Makes In-context Learning Effective for Mathematical Reasoning

• Jiayu Liu 0001
• Zhenya Huang
• Chaokun Wang
• Xunpeng Huang
• ChengXiang Zhai
• Enhong Chen

Owing to the capability of in-context learning, large language models (LLMs) have shown impressive performance across diverse mathematical reasoning benchmarks. However, we find that few-shot demonstrations can sometimes bring negative performance and their effectiveness on LLMs’ reasoning abilities remains unreliable. To this end, in this paper, we aim to theoretically analyze the impact of in-context demonstrations on LLMs’ reasoning performance. We prove that the reasoning efficacy (measured by empirical prediction loss) can be bounded by an LLM-oriented semantic similarity and an inference stability of demonstrations, which is general for both one-shot and few-shot scenarios. Based on this finding, we propose a straightforward, generalizable, and low-complexity demonstration selection method named LMS3. It facilitates to select the most pertinent samples for different LLMs and includes a novel demonstration rejection mechanism to automatically filter out samples that are unsuitable for few-shot learning. Through experiments on three representative benchmarks, two LLM backbones, and multiple few-shot settings, we verify that our LMS3 has superiority and achieves consistent improvements on all datasets, which existing methods have been unable to accomplish. Our code is available at https: //github. com/Ljyustc/LMS3.

IJCAI Conference 2024 Conference Paper

Graph Contrastive Learning with Reinforcement Augmentation

• Ziyang Liu
• Chaokun Wang
• Cheng Wu

Graph contrastive learning (GCL), designing contrastive objectives to learn embeddings from augmented graphs, has become a prevailing method for extracting embeddings from graphs in an unsupervised manner. As an important procedure in GCL, graph data augmentation (GDA) directly affects the model performance on downstream tasks. Currently, the GCL methods typically treat GDA as independent events, neglecting its continuity. In this paper, we regard the GDA in GCL as a Markov decision process and propose a novel graph reinforcement augmentation framework for GCL. Based on this framework, we design a Graph Advantage Actor-Critic (GA2C) model. We conduct extensive experiments to evaluate GA2C on unsupervised learning, transfer learning, and semi-supervised learning. The experimental results demonstrate the performance superiority of GA2C over the state-of-the-art GCL models. Furthermore, we verify that GA2C is more efficient than the other GCL methods with learnable GDA and provide two examples of chemical molecular graphs from ZINC-2M to demonstrate that GA2C generates meaningful augmented views, where the edge weights reflect the importance of chemical bonds in the molecule.

AAAI Conference 2019 Conference Paper

Community Focusing: Yet Another Query-Dependent Community Detection

• Zhuo Wang
• Weiping Wang
• Chaokun Wang
• Xiaoyan Gu
• Bo Li
• Dan Meng

As a major kind of query-dependent community detection, community search finds a densely connected subgraph containing a set of query nodes. As density is the major consideration of community search, most methods of community search often find a dense subgraph with many vertices far from the query nodes, which are not very related to the query nodes. Motivated by this, a new problem called community focusing (CF) is studied. It finds a community where the members are close and densely connected to the query nodes. A distance-sensitive dense subgraph structure called β-attention-core is proposed to remove the vertices loosely connected to or far from the query nodes, and a combinational density is designed to guarantee the density of a subgraph. Then CF is formalized as finding a subgraph with the largest combinational density among the β-attention-core subgraphs containing the query nodes with the largest β. Thereafter, effective methods are devised for CF. Furthermore, a speed-up strategy is developed to make the methods scalable to large networks. Extensive experimental results on real and synthetic networks demonstrate the performance of our methods.

AAAI Conference 2019 Conference Paper

Forbidden Nodes Aware Community Search

• Chaokun Wang
• Junchao Zhu

Community search is an important problem in network analysis, which has attracted much attention in recent years. It starts with some given nodes, pays more attention to local network structures, and gets personalized resultant communities quickly. In this paper, we argue that there are many real scenarios where some nodes are not allowed to appear in the community. Then, we introduce a new concept called forbidden nodes and present a new problem of forbidden nodes aware community search to describe these scenarios.To address the above problem, three methods are proposed, i.e., k-core based FORTE (Forbidden nOdes awaRe communiTy sEarch), k-truss based FORTE and CW based FORTE, where the effects of both forbidden nodes and query nodes are thoroughly considered for each node in the resultant community. The former two methods are able to make use of popular community structures, while the latter is based on a new metric called weighted conductance. The extensive experiments conducted on real data sets demonstrate the effectiveness of the proposed methods.

AAAI Conference 2018 Conference Paper

RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-Imbalanced Labels for Network Embedding

• Zheng Wang
• Xiaojun Ye
• Chaokun Wang
• Yuexin Wu
• Changping Wang
• Kaiwen Liang

Network embedding, aiming to project a network into a lowdimensional space, is increasingly becoming a focus of network research. Semi-supervised network embedding takes advantage of labeled data, and has shown promising performance. However, existing semi-supervised methods would get unappealing results in the completely-imbalanced label setting where some classes have no labeled nodes at all. To alleviate this, we propose a novel semi-supervised network embedding method, termed Relaxed Similarity and Dissimilarity Network Embedding (RSDNE). Specifically, to benefit from the completely-imbalanced labels, RSDNE guarantees both intra-class similarity and inter-class dissimilarity in an approximate way. Experimental results on several real-world datasets demonstrate the superiority of the proposed method.

AAAI Conference 2017 Conference Paper

Multiple Source Detection without Knowing the Underlying Propagation Model

• Zheng Wang
• Chaokun Wang
• Jisheng Pei
• Xiaojun Ye

Information source detection, which is the reverse problem of information diffusion, has attracted considerable research effort recently. Most existing approaches assume that the underlying propagation model is fixed and given as input, which may limit their application range. In this paper, we study the multiple source detection problem when the underlying propagation model is unknown. Our basic idea is source prominence, namely the nodes surrounded by larger proportions of infected nodes are more likely to be infection sources. As such, we propose a multiple source detection method called Label Propagation based Source Identification (LPSI). Our method lets infection status iteratively propagate in the network as labels, and finally uses local peaks of the label propagation result as source nodes. In addition, both the convergent and iterative versions of LPSI are given. Extensive experiments are conducted on several real-world datasets to demonstrate the effectiveness of the proposed method.

IJCAI Conference 2016 Conference Paper

Causality Based Propagation History Ranking in Social Networks

• Zheng Wang
• Chaokun Wang
• Jisheng Pei
• Xiaojun Ye
• Philip S. Yu

In social network sites (SNS), propagation histories which record the information diffusion process can be used to explain to users what happened in their networks. However, these histories easily grow in size and complexity, limiting their intuitive understanding by users. To reduce this information overload, in this paper, we present the problem of propagation history ranking. The goal is to rank participant edges/nodes by their contribution to the diffusion. Firstly, we discuss and adapt Difference of Causal Effects (DCE) as the ranking criterion. Then, to avoid the complex calculation of DCE, we propose a resp-cap ranking strategy by adopting two indicators. The first is responsibility which captures the necessary face of causal effects. We further give an approximate algorithm for this indicator. The second is capability which is defined to capture the sufficient face of causal effects. Finally, promising experimental results are presented to verify the feasibility of our method.

AAAI Conference 2015 Conference Paper

A Personalized Interest-Forgetting Markov Model for Recommendations

• Jun Chen
• Chaokun Wang
• Jianmin Wang

Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.

AAAI Conference 2015 Conference Paper

CrowdMR: Integrating Crowdsourcing with MapReduce for AI-Hard Problems

• Jun Chen
• Chaokun Wang
• Yiyuan Bai

Large-scale distributed computing has made available the resources necessary to solve “AI-hard” problems. As a result, it becomes feasible to automate the processing of such problems, but accuracy is not very high due to the conceptual difficulty of these problems. In this paper, we integrated crowdsourcing with MapReduce to provide a scalable innovative human-machine solution to AI-hard problems, which is called CrowdMR. In CrowdMR, the majority of problem instances are automatically processed by machine while the troublesome instances are redirected to human via crowdsourcing. The results returned from crowdsourcing are validated in the form of CAPTCHA (Completely Automated Public Turing test to Tell Computers and Humans Apart) before adding to the output. An incremental scheduling method was brought forward to combine the results from machine and human in a “pay-as-you-go” way.

AAAI Conference 2015 Conference Paper

Will You “Reconsume” the Near Past? Fast Prediction on Short-Term Reconsumption Behaviors

• Jun Chen
• Chaokun Wang
• Jianmin Wang

The short-term reconsumption behaviors, i. e. “reconsume” the near past, account for a large proportion of people’s activities every day and everywhere. In this paper, we firstly derived four generic features which influence people’s short-term reconsumption behaviors. These features were extracted with respect to different roles in the process of reconsumption behaviors, i. e. users, items and interactions. Then, we brought forward two fast algorithms with the linear and the quadratic kernels to predict whether a user will perform a short-term reconsumption at a specific time given the context. The experimental results show that our proposed algorithms are more accurate in the prediction tasks compared with the baselines. Meanwhile, the time complexity of online prediction of our algorithms is O(1), which enables fast prediction in real-world scenarios. The prediction contributes to more intelligent decision-making, e. g. potential revisited customer identification, personalized recommendation, and information re-finding.

AAAI Conference 2014 Conference Paper

Learning Temporal Dynamics of Behavior Propagation in Social Networks

• Jun Zhang
• Chaokun Wang
• Jianmin Wang