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User profile embedded in the prompt template of personalized recommendation agents play a crucial role in shaping their decision-making process.
Item-based collaborative filtering recommendation algorithms
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Cumulated gain-based evaluation of ir techniques
Kalervo Järvelin and Jaana Kekäläinen. 2002 · 2002
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Herding dynamical weights to learn
Max Welling. 2009 · 2009
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. 2017 · 2010
Earlier work this paper cites.
Greed is still good: Maximizing monotone submodular+supermodular (bp) functions
Wenruo Bai and Jeff Bilmes. 2018 · 2018
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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Earlier work this paper cites.
Practice on long sequential user behavior modeling for click-through rate prediction
Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. 2019 · 2019
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon. 2019 · 2019
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020
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Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction
Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020 · 2020
Earlier work this paper cites.
Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip H. S. Torr, and Puneet K. Dokania. 2020 · 2020
Earlier work this paper cites.
Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite. 2021 · 2021
Earlier work this paper cites.
Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022 · 2022
Earlier work this paper cites.
Deepcore: A comprehensive library for coreset selection in deep learning
Chengcheng Guo, Bo Zhao, and Yanbing Bai. 2022 · 2022
Earlier work this paper cites.
Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S Morcos. 2022 · 2022
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Lift yourself up: Retrieval-augmented text generation with self memory
Xin Cheng, Di Luo, Xiuying Chen, Lemao Liu, Dongyan Zhao, and Rui Yan. 2023 · 2023
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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.
Do llms understand user preferences? evaluating llms on user rating prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023 · 2023
Cited alongside, same era.
Camel: communicative agents for "mind" exploration of large language model society
Coverage-centric coreset selection for high pruning rates
Haizhong Zheng, Rui Liu, Fan Lai, and Atul Prakash. 2023 · 2023
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M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation
Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. 2024 · 2024
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Large language models are zero-shot rankers for recommender systems
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2024 · 2024
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Once: Boosting content-based recommendation with both open- and closed-source large language models
Qijiong Liu, Nuo Chen, Tetsuya Sakai, and Xiao-Ming Wu. 2024 · 2024
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LLM-rec: Personalized recommendation via prompting large language models
Hanjia Lyu, Song Jiang, Hanqing Zeng, Yinglong Xia, Qifan Wang, Si Zhang, Ren Chen, Chris Leung, Jiajie Tang, and Jiebo Luo. 2024 · 2024
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Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023 · 2023
Cited alongside, same era.
Is chatgpt a good recommender? a preliminary study
Junling Liu, Chao Liu, Peilin Zhou, Renjie Lv, Kang Zhou, and Yan Zhang. 2023 · 2023
Cited alongside, same era.
Recranker: Instruction tuning large language model as ranker for top-k recommendation
Sichun Luo, Bowei He, Haohan Zhao, Yinya Huang, Aojun Zhou, Zongpeng Li, Yuanzhang Xiao, Mingjie Zhan, and Linqi Song. 2023 · 2023
Cited alongside, same era.
Lightlm: a lightweight deep and narrow language model for generative recommendation
Kai Mei and Yongfeng Zhang. 2023 · 2023
Cited alongside, same era.
Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023 · 2023
Cited alongside, same era.
Toolllm: Facilitating large language models to master 16000+ real-world apis
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
Cited alongside, same era.
Can ChatGPT assess human personalities? a general evaluation framework
Haocong Rao, Cyril Leung, and Chunyan Miao. 2023 · 2023
Cited alongside, same era.
Integrating summarization and retrieval for enhanced personalization via large language models
Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, and Abhinav Sethy. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
Easyrec: Simple yet effective language models for recommendation
Xubin Ren and Chao Huang. 2024 · 2024
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LaMP: When large language models meet personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2024 · 2024
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Enhancing retrieval and managing retrieval: A four-module synergy for improved quality and efficiency in rag systems
Yunxiao Shi, Xing Zi, Zijing Shi, Haimin Zhang, Qiang Wu, and Min Xu. 2024b · 2024
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Building better ai agents: A provocation on the utilisation of persona in llm-based conversational agents
Guangzhi Sun, Xiao Zhan, and Jose Such. 2024 · 2024
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Rethinking cross-domain sequential recommendation under open-world assumptions
Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, and Junchi Yan. 2024b · 2024
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Agentcf: Collaborative learning with autonomous language agents for recommender systems
Junjie Zhang, Yupeng Hou, Ruobing Xie, Wenqi Sun, Julian McAuley, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2024b · 2024
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Expel: Llm agents are experiential learners
Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. 2024a · 2024
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Cognitive personalized search integrating large language models with an efficient memory mechanism
Yujia Zhou, Qiannan Zhu, Jiajie Jin, and Zhicheng Dou. 2024 · 2024
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Multi-agent collaboration mechanisms: A survey of llms
Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O’Sullivan, and Hoang D. Nguyen. 2025 · 2025
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Recommender systems meet large language model agents: A survey
Xi Zhu, Yu Wang, Hang Gao, Wujiang Xu, Chen Wang, Zhiwei Liu, Kun Wang, Mingyu Jin, Linsey Pang, Qingsong Weng, Philip S. Yu, and Yongfeng Zhang. 2025 · 2025
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