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Large Language Models (LLMs) demonstrate robust capabilities across various fields, leading to a paradigm shift in LLM-enhanced Recommender System (RS).
Pranking with ranking
Koby Crammer and Yoram Singer. 2001 · 2001
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An efficient boosting algorithm for combining preferences
Yoav Freund, Raj D. Iyer, Robert E. Schapire, and Yoram Singer. 2003 · 2003
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Learning to rank using gradient descent
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005 · 2005
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Learning to rank: from pairwise approach to listwise approach
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007 · 2007
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Mcrank: Learning to rank using multiple classification and gradient boosting
Ping Li, Christopher J. C. Burges, and Qiang Wu. 2007 · 2007
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Adarank: a boosting algorithm for information retrieval
Jun Xu and Hang Li. 2007 · 2007
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Softrank: optimizing non-smooth rank metrics
Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka. 2008 · 2008
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Listwise approach to learning to rank: theory and algorithm
Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008 · 2008
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From ranknet to lambdarank to lambdamart: An overview
Christopher JC Burges. 2010 · 2010
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Efficient algorithms for ranking with svms
Olivier Chapelle and S. Sathiya Keerthi. 2010 · 2010
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Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
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Neural collaborative filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
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Neural attentive session-based recommendation
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
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Seq2slate: Re-ranking and slate optimization with rnns
Irwan Bello, Sayali Kulkarni, Sagar Jain, Craig Boutilier, Ed Huai-hsin Chi, Elad Eban, Xiyang Luo, Alan Mackey, and Ofer Meshi. 2018 · 2018
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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian J. McAuley. 2018 · 2018
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The lambdaloss framework for ranking metric optimization
Xuanhui Wang, Cheng Li, Nadav Golbandi, Michael Bendersky, and Marc Najork. 2018 · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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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, and Dan Pei. 2019 · 2019
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Generalized dataweighting via class-level gradient manipulation
Can Chen, Shuhao Zheng, Xi Chen, Erqun Dong, Xue (Steve) Liu, Hao Liu, and Dejing Dou. 2021 · 2021
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JIZHI: A fast and cost-effective model-as-a-service system for web-scale online inference at baidu
Hao Liu, Qian Gao, Jiang Li, Xiaochao Liao, Hao Xiong, Guangxing Chen, Wenlin Wang, Guobao Yang, Zhiwei Zha, Daxiang Dong, Dejing Dou, and Haoyi Xiong. 2021 · 2021
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U-BERT: pre-training user representations for improved recommendation
Zhaopeng Qiu, Xian Wu, Jingyue Gao, and Wei Fan. 2021 · 2021
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
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Zero-shot listwise document reranking with a large language model
Xueguang Ma, Xinyu Zhang, Ronak Pradeep, and Jimmy Lin. 2023 · 2023
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OpenAI. 2023 · 2023
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Large language models are effective text rankers with pairwise ranking prompting
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
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Is chatgpt good at search? investigating large language models as re-ranking agents
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Yupeng Hou, Binbin Hu, Zhiqiang Zhang, and Wayne Xin Zhao. 2022 · 2022
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Improving passage retrieval with zero-shot question generation
Devendra Singh Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, and Luke Zettlemoyer. 2022 · 2022
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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 · 2023
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PALR: personalization aware llms for recommendation
Zheng Chen. 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.
Parade: Passage ranking using demonstrations with llms
Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, and Kai Hui. 2023 · 2023
Cited alongside, same era.
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 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 · 2023
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Llamarec: Two-stage recommendation using large language models for ranking
Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira, Dong Wang, and Even Oldridge. 2023 · 2023
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Generative learning plan recommendation for employees: A performance-aware reinforcement learning approach
Zhi Zheng, Ying Sun, Xin Song, Hengshu Zhu, and Hui Xiong. 2023b · 2023
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Interaction-aware drug package recommendation via policy gradient
Zhi Zheng, Chao Wang, Tong Xu, Dazhong Shen, Penggang Qin, Xiangyu Zhao, Baoxing Huai, Xian Wu, and Enhong Chen. 2023c · 2023
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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 J. McAuley, and Wayne Xin Zhao. 2024b · 2024
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Setrank: A setwise bayesian approach for collaborative ranking in recommender system
Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Enhong Chen, and Hui Xiong. 2024 · 2024
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Exploring large language model for graph data understanding in online job recommendations
Likang Wu, Zhaopeng Qiu, Zhi Zheng, Hengshu Zhu, and Enhong Chen. 2024 · 2024
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Cong Xu, Zhangchi Zhu, Jun Wang, Jianyong Wang, and Wei Zhang. 2024 · 2024
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Harnessing large language model in text-rich sequential recommendation
Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu, Hengshu Zhu, and Hui Xiong. 2024 · 2024
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