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Evaluating and iterating upon recommender systems is crucial, yet traditional A/B testing is resource-intensive, and offline methods struggle with dynamic user-platform interactions.
Recsim: A configurable simulation platform for recommender systems
Eugene Ie, Chih-Wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, and Craig Boutilier. 2019 · 1909
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Term-weighting approaches in automatic text retrieval
Gerard Salton and Christopher Buckley. 1988 · 1988
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
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Introduction to recommender systems handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2010 · 2010
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The effect of online consumer reviews on new product sales
Geng Cui, Hon-Kwong Lui, and Xiaoning Guo. 2012 · 2012
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
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Image-based recommendations on styles and substitutes
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015 · 2015
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, and 1 others. 2023 · 2015
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Variational autoencoders for collaborative filtering
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018 · 2018
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Generative adversarial user model for reinforcement learning based recommendation system
Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, and Le Song. 2019 · 2019
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The effect of online reviews on product sales: A joint sentiment-topic analysis
Xiaolin Li, Chaojiang Wu, and Feng Mai. 2019 · 2019
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Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning
Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, and Anxiang Zeng. 2019 · 2019
Cited alongside, same era.
Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020 · 2020
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Keeping dataset biases out of the simulation: A debiased simulator for reinforcement learning based recommender systems
Jin Huang, Harrie Oosterhuis, Maarten de Rijke, and Herke van Hoof. 2020 · 2020
Cited alongside, same era.
How useful are reviews for recommendation? a critical review and potential improvements
Noveen Sachdeva and Julian McAuley. 2020 · 2020
Cited alongside, same era.
Recommender systems based on collaborative filtering using review texts—a survey
Mehdi Srifi, Ahmed Oussous, Ayoub Ait Lahcen, and Salma Mouline. 2020 · 2020
Cited alongside, same era.
Training socially aligned language models in simulated human society
Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M. Dai, Diyi Yang, and Soroush Vosoughi. 2023 · 2023
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Communicative agents for software development
Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
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Autogen: Enabling next-gen LLM applications via multi-agent conversation framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023 · 2023
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Exploring large language models for communication games: An empirical study on werewolf
Yuzhuang Xu, Shuo Wang, Peng Li, Fuwen Luo, Xiaolong Wang, Weidong Liu, and Yang Liu. 2023 · 2023
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How do product attributes and reviews moderate the impact of recommender systems through purchase stages?
Dokyun Lee and Kartik Hosanagar. 2021 · 2021
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Top-n recommendation with counterfactual user preference simulation
Mengyue Yang, Quanyu Dai, Zhenhua Dong, Xu Chen, Xiuqiang He, and Jun Wang. 2021 · 2021
Cited alongside, same era.
Uctopic: Unsupervised contrastive learning for phrase representations and topic mining
Jiacheng Li, Jingbo Shang, and Julian McAuley. 2022 · 2022
Cited alongside, same era.
Mindsim: User simulator for news recommenders
Xufang Luo, Zheng Liu, Shitao Xiao, Xing Xie, and Dongsheng Li. 2022 · 2022
Cited alongside, same era.
Reinforcement learning based recommender systems: A survey
Mohammad Mehdi Afsar, Trafford Crump, and Behrouz H. Far. 2023 · 2023
Cited alongside, same era.
Agentsims: An open-source sandbox for large language model evaluation
Jiaju Lin, Haoran Zhao, Aochi Zhang, Yiting Wu, Huqiuyue Ping, and Qin Chen. 2023 · 2023
Cited alongside, same era.
Aligning with human judgement: The role of pairwise preference in large language model evaluators
Yinhong Liu, Han Zhou, Zhijiang Guo, Ehsan Shareghi, Ivan Vulić, Anna Korhonen, and Nigel Collier. 2024a
Cited in the paper.
Gptscore: Evaluate as you desire
Jinlan Fu, See Kiong Ng, Zhengbao Jiang, and Pengfei Liu. 2024 · 2024
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Oasis: Open agents social interaction simulations on one million agents
Ziyi Yang, Zaibin Zhang, Zirui Zheng, Yuxian Jiang, Ziyue Gan, Zhiyu Wang, Zijian Ling, Jinsong Chen, Martz Ma, Bowen Dong, and 1 others. 2024 · 2024
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On generative agents in recommendation
An Zhang, Yuxin Chen, Leheng Sheng, Xiang Wang, and Tat-Seng Chua. 2024 · 2024
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Simuser: Simulating user behavior with large language models for recommender system evaluation
Nicolas Bougie and Narimasa Watanabe. 2025 · 2025
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Yuhan Liu, Yuxuan Liu, Xiaoqing Zhang, Xiuying Chen, and Rui Yan. 2025 · 2025
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User behavior simulation with large language model-based agents
Lei Wang, Jingsen Zhang, Hao Yang, Zhi-Yuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Hao Sun, Ruihua Song, and 1 others. 2025 · 2025
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The rise and potential of large language model based agents: A survey
Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, and 1 others. 2025 · 2025
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