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Large language models (LLMs) have attracted significant attention in recommendation systems.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis. 2018 · 2018
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Revisiting Negative Sampling vs. Non-sampling in Implicit Recommendation
Chong Chen, Weizhi Ma, Min Zhang, Chenyang Wang, Yiqun Liu, and Shaoping Ma. 2023b · 2023
Earlier work this paper cites.
Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023a · 2023
Earlier work this paper cites.
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen, Xiangnan He, Wenqiang Lei, Biao Li, Yuan Zhang, and Peng Jiang. 2023c · 2023
Earlier work this paper cites.
Chat-rec: Towards interactive and explainable llms-augmented recommender system
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023b · 2023
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Fairness in Recommendation: Foundations, Methods, and Applications
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2023 · 2023
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On the Theories Behind Hard Negative Sampling for Recommendation. In Proceedings of the ACM Web Conference 2023 (WWW ’23) . 812–822
Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, and Xiangnan He. 2023 · 2023
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A Survey on the Fairness of Recommender Systems
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023 · 2023
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Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys ’23) . 993–999
Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
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A general theoretical paradigm to understand learning from human preferences. In International Conference on Artificial Intelligence and Statistics (AISTATS ’24) . PMLR, 4447–4455
Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, and Daniele Calandriello. 2024 · 2024
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Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24) . 76–86
Zhuoxi Bai, Ning Wu, Fengyu Cai, Xinyi Zhu, and Yun Xiong. 2024 · 2024
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Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation
Keqin Bao, Jizhi Zhang, Yang Zhang, Xinyue Huo, Chong Chen, and Fuli Feng. 2024 · 2024
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FLOW: A Feedback LOop FrameWork for Simultaneously Enhancing Recommendation and User Agents
Shihao Cai, Jizhi Zhang, Keqin Bao, Chongming Gao, and Fuli Feng. 2024 · 2024
Cited alongside, same era.
Human alignment of large language models through online preference optimisation. In Proceedings of the 41st International Conference on Machine Learning (ICML ’24) . Article 211, 27 pages
Daniele Calandriello, Zhaohan Daniel Guo, Remi Munos, Mark Rowland, Yunhao Tang, Bernardo Avila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, and Bilal Piot. 2024 · 2024
Cited alongside, same era.
Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’24) . 6437–6447
Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, and Jun Xu. 2024 · 2024
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
Smaug: Fixing failure modes of preference optimisation with dpo-positive
Arka Pal, Deep Karkhanis, Samuel Dooley, Manley Roberts, Siddartha Naidu, and Colin White. 2024 · 2024
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Iterative Reasoning Preference Optimization
Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He, Sainbayar Sukhbaatar, and Jason Weston. 2024 · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
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Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment
Hao Sun and Mihaela van der Schaar. 2024 · 2024
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Fairness Matters: A look at LLM-generated group recommendations. In Proceedings of the 18th ACM Conference on Recommender Systems (RecSys ’24) . 993–998
Antonela Tommasel. 2024 · 2024
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Cited alongside, same era.
Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective
Duanyu Feng, Bowen Qin, Chen Huang, Zheng Zhang, and Wenqiang Lei. 2024 · 2024
Cited alongside, same era.
Bias and Fairness in Large Language Models: A Survey
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed. 2024 · 2024
Cited alongside, same era.
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24) . 2311–2315
Binzong Geng, Zhaoxin Huan, Xiaolu Zhang, Yong He, Liang Zhang, Fajie Yuan, Jun Zhou, and Linjian Mo. 2024 · 2024
Cited alongside, same era.
Large Language Models are Zero-Shot Rankers for Recommender Systems. In Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part II . 364–381
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024 · 2024
Cited alongside, same era.
Supply-side equilibria in recommender systems. In Proceedings of the 37th International Conference on Neural Information Processing Systems (NeurIPS ’23) . Article 642, 12 pages
Meena Jagadeesan, Nikhil Garg, and Jacob Steinhardt. 2024 · 2024
Cited alongside, same era.
Item-side Fairness of Large Language Model-based Recommendation System. In Proceedings of the ACM on Web Conference 2024 (WWW ’24) . 4717–4726
Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, and Xiangnan He. 2024 · 2024
Cited alongside, same era.
RosePO: Aligning LLM-based Recommenders with Human Values
Jiayi Liao, Xiangnan He, Ruobing Xie, Jiancan Wu, Yancheng Yuan, Xingwu Sun, Zhanhui Kang, and Xiang Wang. 2024 · 2024
Cited alongside, same era.
Large Language Models Enhanced Sequential Recommendation for Long-tail User and Item
Qidong Liu, Xian Wu, Xiangyu Zhao, Yejing Wang, Zijian Zhang, Feng Tian, and Yefeng Zheng. 2024 · 2024
Cited alongside, same era.
Closest in time.
Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond
Qi Wang, Jindong Li, Shiqi Wang, Qianli Xing, Runliang Niu, He Kong, Rui Li, Guodong Long, Yi Chang, and Chengqi Zhang. 2024b · 2024
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LLMRec: Large Language Models with Graph Augmentation for Recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM ’24) . 806–815
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024 · 2024
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A Survey on Large Language Models for Recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, and Enhong Chen. 2024 · 2024
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A Study of Implicit Ranking Unfairness in Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024 . 7957–7970
Chen Xu, Wenjie Wang, Yuxin Li, Liang Pang, Jun Xu, and Tat-Seng Chua. 2024 · 2024
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Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms
Fan Yao, Yiming Liao, Jingzhou Liu, Shaoliang Nie, Qifan Wang, Haifeng Xu, and Hongning Wang. 2024 · 2024
Closest in time.
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems. In Proceedings of the ACM Web Conference 2024 (WWW ’24) . 3679–3689
Junjie Zhang, Yupeng Hou, Ruobing Xie, Wenqi Sun, Julian McAuley, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2024 · 2024
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A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yanchen Luo, Chong Chen, Fuli Feng, and Qi Tian. 2025 · 2025
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DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems
Jiaju Chen, Chongming Gao, Shuai Yuan, Shuchang Liu, Qingpeng Cai, and Peng Jiang. 2025 · 2025
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Self-Play Preference Optimization for Language Model Alignment. In The Thirteenth International Conference on Learning Representations (ICLR ’2025)
Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, and Quanquan Gu. 2025 · 2025
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