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Large Language Models (LLMs) have exhibited remarkable performance across a wide range of domains, motivating research into their potential for recommendation systems.
“Cumulated gain-based evaluation of ir techniques,”
Kalervo Järvelin and Jaana Kekäläinen, · 2002
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“A support vector method for optimizing average precision,”
Yisong Yue, Thomas Finley, Filip Radlinski, and Thorsten Joachims, · 2007
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“Learning a deep listwise context model for ranking refinement,”
Qingyao Ai, Keping Bi, Jiafeng Guo, and W Bruce Croft, · 2018
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“Deep interest network for click-through rate prediction,”
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai, · 2018
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“Personalized re-ranking for recommendation,”
Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, et al., · 2019
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“Language models are few-shot learners,”
Tom B Brown, · 2020
Earlier work this paper cites.
“Setrank: Learning a permutation-invariant ranking model for information retrieval,”
Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, and Jirong Wen, · 2020
Earlier work this paper cites.
“Training a helpful and harmless assistant with reinforcement learning from human feedback,”
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al., · 2022
Earlier work this paper cites.
“Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5),”
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang, · 2022
Earlier work this paper cites.
“A survey of large language models,”
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al., · 2023
Cited alongside, same era.
“Recommender systems in the era of large language models (llms),”
Zihuai Zhao, Wenqi Fan, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, et al., · 2023
Cited alongside, same era.
“Tallrec: An effective and efficient tuning framework to align large language model with recommendation,”
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He, · 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
Cited alongside, same era.
“Vip5: Towards multimodal foundation models for recommendation,”
“A survey of generative search and recommendation in the era of large language models,”
Yongqi Li, Xinyu Lin, Wenjie Wang, Fuli Feng, Liang Pang, Wenjie Li, Liqiang Nie, Xiangnan He, and Tat-Seng Chua, · 2024
Closest in time.
“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
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“Aligning large language models for controllable recommendations,”
Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, and Xing Xie, · 2024
Closest in time.
“Kellmrec: Knowledge-enhanced large language models for recommendation,”
Weiqing Luo, Chonggang Song, Lingling Yi, and Gong Cheng, · 2024
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“Large language models as data augmenters for cold-start item recommendation,”
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Shijie Geng, Juntao Tan, Shuchang Liu, Zuohui Fu, and Yongfeng Zhang, · 2023
Cited alongside, same era.
“Gpt4rec: A generative framework for personalized recommendation and user interests interpretation,”
Jinming Li, Wentao Zhang, Tian Wang, Guanglei Xiong, Alan Lu, and Gerard Medioni, · 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
Cited alongside, same era.
“A first look at llm-powered generative news recommendation,”
Qijiong Liu, Nuo Chen, Tetsuya Sakai, and Xiao-Ming Wu, · 2023
Cited alongside, same era.
“Taggpt: Large language models are zero-shot multimodal taggers,”
Chen Li, Yixiao Ge, Jiayong Mao, Dian Li, and Ying Shan, · 2023
Cited alongside, same era.
“Towards open-world recommendation with knowledge augmentation from large language models,”
Yunjia Xi, Weiwen Liu, Jianghao Lin, Xiaoling Cai, Hong Zhu, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, and Yong Yu,
Cited in the paper.
Jianling Wang, Haokai Lu, James Caverlee, Ed H Chi, and Minmin Chen, · 2024
Closest in time.
“Representation learning with large language models for recommendation,”
Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang, · 2024
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“RLAIF vs. RLHF: Scaling reinforcement learning from human feedback with AI feedback,”
Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Ren Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash, · 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
Closest in time.
“A general theoretical paradigm to understand learning from human preferences,”
Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, and Daniele Calandriello, · 2024
Closest in time.