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The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems.
BPR: Bayesian personalized ranking from implicit feedback. In UAI
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Understanding bag-of-words model: a statistical framework. In IJMLC
Yin Zhang, Rong Jin, and Zhi-Hua Zhou. 2010 · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
A survey on solving cold start problem in recommender systems. In ICCCA
Jyotirmoy Gope and Sanjay Kumar Jain. 2017 · 2017
Earlier work this paper cites.
Neural collaborative filtering. In WWW
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Self-attentive sequential recommendation. In ICDM
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding. In ACL
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. In KDD
Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019 · 2019
Earlier work this paper cites.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In EMNLP
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Earlier work this paper cites.
Neural graph collaborative filtering. In SIGIR
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
Earlier work this paper cites.
Sampling-bias-corrected neural modeling for large corpus item recommendations. In RecSys
Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed Chi. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners. In NeurIPS
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
How to learn item representation for cold-start multimedia recommendation?. In MM
Xiaoyu Du, Xiang Wang, Xiangnan He, Zechao Li, Jinhui Tang, and Tat-Seng Chua. 2020 · 2020
Cited alongside, same era.
Data augmentation for deep graph learning: A survey. In ACM SIGKDD Explorations Newsletter
Kaize Ding, Zhe Xu, Hanghang Tong, and Huan Liu. 2022 · 2022
Cited alongside, same era.
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In ResSys
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback. In NeurIPS
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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
Later among the works it cites.
VIP5: Towards Multimodal Foundation Models for Recommendation
Shijie Geng, Juntao Tan, Shuchang Liu, Zuohui Fu, and Yongfeng Zhang. 2023 · 2023
Later among the works it cites.
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. 2023 · 2023
Later among the works it cites.
Ehsan Kamalloo, Nouha Dziri, Charles LA Clarke, and Davood Rafiei. 2023 · 2023
Later among the works it cites.
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Tobias Schnabel, Mengting Wan, and Longqi Yang. 2022 · 2022
Cited alongside, same era.
Learning to Augment for Casual User Recommendation. In TheWebConf
Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H Chi, and Minmin Chen. 2022 · 2022
Cited alongside, same era.
Tiny-newsrec: Effective and efficient plm-based news recommendation. In EMNLP
Yang Yu, Fangzhao Wu, Chuhan Wu, Jingwei Yi, and Qi Liu. 2022 · 2022
Cited alongside, same era.
Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder. In SIGIR
Xu Zhao, Yi Ren, Ying Du, Shenzheng Zhang, and Nian Wang. 2022 · 2022
Cited alongside, same era.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
Cited alongside, same era.
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 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. 2023a
Cited in the paper.
Junling Liu, Chao Liu, Renjie Lv, Kang Zhou, and Yan Zhang. 2023 · 2023
Later among the works it cites.
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, et al · 2023
Later among the works it cites.
Take a Fresh Look at Recommender Systems from an Evaluation Standpoint. In SIGIR
Aixin Sun. 2023 · 2023
Later among the works it cites.
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, and Yong Yu. 2023 · 2023
Later among the works it cites.
Where to Go Next for Recommender Systems? ID-vs. Modality-based recommender models revisited
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023 · 2023
Later among the works it cites.
A survey of large language models. In arXiv preprint arXiv:2303.18223
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Later among the works it cites.