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Planning for both immediate and long-term benefits becomes increasingly important in recommendation.
Returning is believing: Optimizing long-term user engagement in recommender systems. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 1927–1936
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Asynchronous Methods for Deep Reinforcement Learning. In ICML (JMLR Workshop and Conference Proceedings, Vol. 48) . JMLR.org, 1928–1937
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Playing Atari with Deep Reinforcement Learning
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Reinforcement Learning in Large Discrete Action Spaces
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In IJCAI . ijcai.org, 1725–1731
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Proximal Policy Optimization Algorithms
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Self-Attentive Sequential Recommendation. In ICDM . IEEE Computer Society, 197–206
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Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
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Deep reinforcement learning for page-wise recommendations. In RecSys . ACM, 95–103
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DRN: A Deep Reinforcement Learning Framework for News Recommendation. In WWW . ACM, 167–176
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Top-K Off-Policy Correction for a REINFORCE Recommender System. In WSDM . ACM, 456–464
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Benchmarking Batch Deep Reinforcement Learning Algorithms
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SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets. In IJCAI . ijcai.org, 2592–2599
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019
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Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In EMNLP/IJCNLP (1) . Association for Computational Linguistics, 188–197
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Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020 · 2020
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Conservative Q-Learning for Offline Reinforcement Learning. In NeurIPS
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine. 2020 · 2020
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Critic Regularized Regression. In NeurIPS
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Self-Supervised Reinforcement Learning for Recommender Systems. In SIGIR . ACM, 931–940
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Advances and Challenges in Conversational Recommender Systems: A Survey
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Cited alongside, same era.
Planning with Large Language Models via Corrective Re-prompting
Shreyas Sundara Raman, Vanya Cohen, Eric Rosen, Ifrah Idrees, David Paulius, and Stefanie Tellex. 2022 · 2022
Cited alongside, same era.
Surrogate for Long-Term User Experience in Recommender Systems. In KDD . ACM, 4100–4109
Yuyan Wang, Mohit Sharma, Can Xu, Sriraj Badam, Qian Sun, Lee Richardson, Lisa Chung, Ed H. Chi, and Minmin Chen. 2022 · 2022
Cited alongside, same era.
Dynamics-Aware Adaptation for Reinforcement Learning Based Cross-Domain Interactive Recommendation. In SIGIR . ACM, 290–300
Junda Wu, Zhihui Xie, Tong Yu, Handong Zhao, Ruiyi Zhang, and Shuai Li. 2022 · 2022
Cited alongside, same era.
Dynamic Causal Collaborative Filtering. In CIKM . ACM, 2301–2310
Shuyuan Xu, Juntao Tan, Zuohui Fu, Jianchao Ji, Shelby Heinecke, and Yongfeng Zhang. 2022 · 2022
Is chatgpt a good recommender? a preliminary study
Junling Liu, Chao Liu, Renjie Lv, Kang Zhou, and Yan Zhang. 2023 · 2023
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Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym
Junjie Sheng, Zixiao Huang, Chuyun Shen, Wenhao Li, Yun Hua, Bo Jin, Hongyuan Zha, and Xiangfeng Wang. 2023 · 2023
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Reflexion: Language Agents with Verbal Reinforcement Learning
Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
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AdaPlanner: Adaptive Planning from Feedback with Language Models
Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang. 2023 · 2023
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Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Cited alongside, same era.
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yancheng Luo, Fuli Feng, Xiangnan He, and Qi Tian. 2023a · 2023
Cited alongside, same era.
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023b · 2023
Cited alongside, same era.
Large Language Models can Implement Policy Iteration
Ethan Brooks, Logan Walls, Richard L. Lewis, and Satinder Singh. 2023 · 2023
Cited alongside, same era.
Adap- τ \tau : Adaptively Modulating Embedding Magnitude for Recommendation. In WWW . ACM, 1085–1096
Jiawei Chen, Junkang Wu, Jiancan Wu, Xuezhi Cao, Sheng Zhou, and Xiangnan He. 2023 · 2023
Cited alongside, same era.
Uncovering ChatGPT’s Capabilities in Recommender Systems
Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023 · 2023
Cited alongside, same era.
Recommender systems in the era of large language models (llms)
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023 · 2023
Cited alongside, same era.
Hugo Touvron et al · 2023
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Recmind: Large language model powered agent for recommendation
Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, and Yingzhen Yang. 2023b · 2023
Later among the works it cites.
Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang. 2023a · 2023
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Llmrec: Large language models with graph augmentation for recommendation
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2023 · 2023
Later among the works it cites.
Exploring large language model for graph data understanding in online job recommendations
Likang Wu, Zhaopeng Qiu, Zhi Zheng, Hengshu Zhu, and Enhong Chen. 2023a · 2023
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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, et al · 2023
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Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, and Yong Yu. 2023 · 2023
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ReAct: Synergizing Reasoning and Acting in Language Models. In ICLR . OpenReview.net
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. 2023 · 2023
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Large Language Model Is Semi-Parametric Reinforcement Learning Agent
Danyang Zhang, Lu Chen, Situo Zhang, Hongshen Xu, Zihan Zhao, and Kai Yu. 2023b · 2023
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Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation
Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023a · 2023
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Recommendation as instruction following: A large language model empowered recommendation approach
Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023d · 2023
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Collm: Integrating collaborative embeddings into large language models for recommendation
Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang, and Xiangnan He. 2023c · 2023
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ExpeL: LLM Agents Are Experiential Learners
Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. 2023 · 2023
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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. 2024 · 2024
Closest in time.
EasyRL4Rec: A User-Friendly Code Library for Reinforcement Learning Based Recommender Systems
Yuanqing Yu, Chongming Gao, Jiawei Chen, Heng Tang, Yuefeng Sun, Qian Chen, Weizhi Ma, and Min Zhang. 2024 · 2024
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Off-Policy Deep Reinforcement Learning without Exploration. In ICML (Proceedings of Machine Learning Research, Vol. 97) . PMLR, 2052–2062
Scott Fujimoto, David Meger, and Doina Precup. 2019b · 2062
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