1. RecExplainer: Aligning Large Language Models for Explaining Recommendation Models Yuxuan Lei (University of Science and Technology of China) et al.
2. Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation Xinyu Lin (National University of Singapore) et al.
3. CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation Junda Wu (University of California San Diego) et al.
4. Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System Sein Kim (Korea Advanced Institute of Science and Technology) et al.
5. DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation Kounianhua Du (Shanghai Jiao Tong University) et al.
6. Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning Xiao Han (City University of Hong Kong) et al.
7. EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration Ye Wang (Zhejiang University) et al.
8. CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent Liang-bo Ning (The Hong Kong Polytechnic University) et al.
图推荐算法
9. Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning Jiakai Tang (Gaoling School of Artificial Intelligence, Renmin University of China) et al.
10. GPFedRec: Graph-Guided Personalization for Federated Recommendation Chunxu Zhang (College of Computer Science and Technology, Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University) et al.
11. How Powerful is Graph Filtering for Recommendation Shaowen Peng (Nara Institute of Science and Technology) et al.
12. Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering Yihong Wu (Université de Montréal) et al.
13. Graph Bottlenecked Social Recommendation Yonghui Yang (Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology) et al.
14. When Box Meets Graph Neural Network in Tag-aware Recommendation Fake Lin (University of Science and Technology of China) et al.
15. Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations Linxin Guo (Chongqing University) et al.
16. Customizing Graph Neural Network for CAD Assembly Recommendation Fengqi Liang (Beijing University of Post and Telecommunication); Huan Zhao (4Paradigm Inc.) et al.
序列推荐算法
17. Explicit and Implicit Modeling via Dual-Path Transformer for Behavior Set-informed Sequential Recommendation Ming Chen (College of Computer Science and Software Engineering, Shenzhen University) et al.
18. Probabilistic Attention for Sequential Recommendation Yuli Liu (Qinghai University, Qinghai Provincial Key Laboratory of Media Integration Technology and Communication) et al.
19. Dataset Regeneration for Sequential Recommendation Mingjia Yin (University of Science and Technology of China & State Key Laboratory of Cognitive Intelligence) et al.
20. Disentangled Multi-interest Representation Learning for Sequential Recommendation Yingpeng Du (Nanyang Technological University) et al.
21. Pre-Training with Transferable Attention for Addressing Market Shifts in Cross-Market Sequential Recommendation Chen Wang (University of Illinois Chicago) et al.
22. ROTAN: A Rotation-based Temporal Attention Network for Time-Specific Next POI Recommendation Shanshan Feng (Centre for Frontier AI Research, ASTAR, Institute of High Performance Computing, ASTAR) et al.
23. Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations Jing Long (The University of Queensland) et al.
24. Going Where, by Whom, and at What Time: Next Location Prediction Considering User Preference and Temporal Regularity Tianao Sun (School of Software, Shandong University) et al.
25. DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation Kairui Fu (Zhejiang University) et al.
推荐公平性&安全性&隐私性
26. Where Have You Been? A Study of Privacy Risk for Point-of-Interest Recommendation Kunlin Cai (University of California, Los Angeles) et al.
27. Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems Zhichen Xiang (College of Management and Economics, Tianjin University, Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University) et al.
28. Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks Zongwei Wang (Chongqing University) et al.
29. Debiased Recommendation with Noisy Feedback Haoxuan Li (Peking University) et al.
30. A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation Shoujin Wang (University of Technology Sydney) et al.
31. Harm Mitigation in Recommender Systems under User Preference Dynamics Jerry Chee (Cornell University) et al.
32. Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time Haiyuan Zhao (School of Information, Renmin University of China) et al.
计算广告
33. Robust Auto-Bidding Strategies for Online Advertising Qilong Lin (Shanghai Jiao Tong University) et al.
34. InLN: Knowledge-aware Incremental Leveling Network for Dynamic Advertising Xujia Li (Hong Kong University of Science and Technology) et al.
35. Joint Auction in the Online Advertising Market Zhen Zhang (Gaoling School of Artificial Intelligence, Renmin University of China) et al.
36. Auctions with LLM Summaries Avinava Dubey (Google Research) et al.
37. Bi-Objective Contract Allocation for Guaranteed Delivery Advertising Yan Li (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences) et al.
38. Optimized Cost Per Click in Online Advertising: A Theoretical Analysis Kaichen Zhang (Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)) et al.
39. Truthful Bandit Mechanisms for Repeated Two-stage Ad Auctions Haoming Li (Shanghai Jiaotong University) et al.
40. An Efficient Local Search Algorithm for Large GD Advertising Inventory Allocation with Multilinear Constraints Xiang He (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of Sciences) et al.
推荐去偏&去噪
41. Self-Supervised Denoising through Independent Cascade Graph Augmentation for Robust Social Recommendation Youchen Sun (Nanyang Technological University) et al.
42. Double Correction Framework for Denoising Recommendation Zhuangzhuang He (Hefei University of Technology) et al.
43. Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback Guipeng Xv (School of Informatics, Xiamen University) et al.
44. Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias Miaomiao Cai (Hefei University of Technology) et al.
强化学习推荐系统
45. Privileged Knowledge State Distillation for Reinforcement Learning-based Educational Path Recommendation Qingyao Li (Shanghai Jiao Tong University) et al.
46. On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top- Recommendation Olivier Jeunen (ShareChat) et al.
47. Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation Xiaocong Chen (Data 61, CSIRO) et al.
48. Conversational Dueling Bandits in Generalized Linear Models Shuhua Yang (University of Science and Technology of China) et al.
其他分类
49. Natural Language Explainable Recommendation with Robustness Enhancement Jingsen Zhang (Gaoling School of Artificial Intelligence, Renmin University of China) et al.
50. Rotative Factorization Machines Zhen Tian (Gaoling School of Artificial Intelligence, Renmin University of China, Beijing Key Laboratory of Big Data Management and Analysis Methods) et al.
51. Mitigating Negative Transfer in Cross-Domain Recommendation via Knowledge Transferability Enhancement Zijian Song (School of CS, Peking University, National Engineering Laboratory for Big Data Analysis and Applications, Peking University) et al.
52. Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method Chen Yang (Nanbeige Lab, BOSS Zhipin, Gaoling School of Artificial Intelligence, Renmin University of China) et al.
53. Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions Yaqing Wang (Baidu Research, Baidu Inc.) et al.
54. Automatic Multi-Task Learning Framework with Neural Architecture Search in Recommendations Shen Jiang (State Key Laboratory for Novel Software Technology, Nanjing University) et al.
55. Continual Collaborative Distillation for Recommender System Gyuseok Lee (Pohang University of Science and Technology) et al.
56. Relevance Meets Diversity: A User-Centric Framework for Knowledge Exploration Through Recommendations Erica Coppolillo (Department of Computer Science, University of Calabria, ICAR-CNR) et al.
57. Shopping Trajectory Representation Learning with Pre-training for E-commerce Customer Understanding and Recommendation Yankai Chen (Department of Computer Science and Engineering, The Chinese University of Hong Kong) et al.
58. Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective Haotian Zhang (State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China) et al.
59. User Welfare Optimization in Recommender Systems with Competing Content Creators Fan Yao (University of Virginia) et al.
