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When adapting Large Language Models for Recommendation (LLMRec), it is crucial to integrate collaborative information.
Learning to hash with graph neural networks for recommender systems
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Item-based collaborative filtering recommendation algorithms
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
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Fism: factored item similarity models for top-n recommender systems
Santosh Kabbur, Xia Ning, and George Karypis. 2013 · 2013
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The movielens datasets: History and context
F. Maxwell Harper and Joseph A. Konstan. 2016 · 2016
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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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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian J. McAuley. 2018 · 2018
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Umap: Uniform manifold approximation and projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. 2018 · 2018
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Personalized top-n sequential recommendation via convolutional sequence embedding
Jiaxi Tang and Ke Wang. 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
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Deep interest evolution network for click-through rate prediction
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019 · 2019
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Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020 · 2020
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Roadmap of security threats between ipv4/ipv6
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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
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Improving recommendation fairness via data augmentation
Prompt learning for news recommendation
Zizhuo Zhang and Bang Wang. 2023 · 2023
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Language modeling is compression
Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, Elliot Catt, Tim Genewein, Christopher Mattern, Jordi Grau-Moya, Li Kevin Wenliang, Matthew Aitchison, Laurent Orseau, Marcus Hutter, and Joel Veness. 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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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 · 2024
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Llara: Large language-recommendation assistant
Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, and Xiangnan He. 2024 · 2024
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Lei Chen, Le Wu, Kun Zhang, Richang Hong, Defu Lian, Zhiqiang Zhang, Jun Zhou, and Meng Wang. 2023 · 2023
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Leveraging large language models for sequential recommendation
Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, and Marios Fragkoulis. 2023 · 2023
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How to index item ids for recommendation foundation models
Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2023 · 2023
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Recommender systems with generative retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, and Maheswaran Sathiamoorthy. 2023 · 2023
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Thrilled by your progress! large language models (gpt-4) no longer struggle to pass assessments in higher education programming courses
Jaromir Savelka, Arav Agarwal, Marshall An, Chris Bogart, and Majd Sakr. 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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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, Xiangnaan He, and Qi Tian. 2023a
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Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation
Jianghao Lin, Rong Shan, Chenxu Zhu, Kounianhua Du, Bo Chen, Shigang Quan, Ruiming Tang, Yong Yu, and Weinan Zhang. 2024a · 2024
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Large language models are learnable planners for long-term recommendation
Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi Zhang, Qifan Wang, and Fuli Feng. 2024 · 2024
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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. 2024 · 2024
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Adapting large language models by integrating collaborative semantics for recommendation
Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, and Ji-Rong Wen. 2024a · 2024
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Harnessing large language models for text-rich sequential recommendation
Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu, Hengshu Zhu, and Hui Xiong. 2024b · 2024
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Collaborative large language model for recommender systems
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. 2024 · 2024
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