Fetching the paper…
Reading the bibliography…
The recent advancements in Large Language Models (LLMs) have sparked interest in harnessing their potential within recommender systems.
S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. In CIKM ’20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020 . ACM, 1893–1902
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 · 1902
Earlier work this paper cites.
“Cloze Procedure”: A New Tool for Measuring Readability
Wilson L. Taylor. 1953 · 1953
Earlier work this paper cites.
GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In CSCW ’94, Proceedings of the Conference on Computer Supported Cooperative Work, Chapel Hill, NC, USA, October 22-26, 1994 . ACM, 175–186
Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994 · 1994
Earlier work this paper cites.
GroupLens: Applying Collaborative Filtering to Usenet News
Joseph A. Konstan, Bradley N. Miller, David A. Maltz, Jonathan L. Herlocker, Lee R. Gordon, and John Riedl. 1997 · 1997
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms. In Proceedings of the Tenth International World Wide Web Conference, WWW 10, Hong Kong, China, May 1-5, 2001 . ACM, 285–295
Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
Greg Linden, Brent Smith, and Jeremy York. 2003 · 2003
Earlier work this paper cites.
Item-based top- N recommendation algorithms
Mukund Deshpande and George Karypis. 2004 · 2004
Earlier work this paper cites.
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
Gediminas Adomavicius and Alexander Tuzhilin. 2005 · 2005
Earlier work this paper cites.
Matrix Factorization Techniques for Recommender Systems
Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009 . AUAI Press, 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Factorization Machines
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, April 26-30, 2010 . ACM, 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Image-Based Recommendations on Styles and Substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, August 9-13, 2015 . ACM, 43–52
Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015 · 2015
Earlier work this paper cites.
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation. In IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain . IEEE Computer Society, 191–200
Ruining He and Julian J. McAuley. 2016 · 2016
Earlier work this paper cites.
Session-based Recommendations with Recurrent Neural Networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
Self-Attentive Sequential Recommendation. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17-20, 2018 . IEEE Computer Society, 197–206
Wang-Cheng Kang and Julian J. McAuley. 2018 · 2018
Earlier work this paper cites.
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, February 5-9, 2018 . ACM, 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers) . Association for Computational Linguistics, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019 . ACM, 1441–1450
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Cited alongside, same era.
Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Recommender Systems in the Era of Large Language Models (LLMs)
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Jiliang Tang, and Qing Li. 2023 · 2023
Closest in time.
Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023 · 2023
Closest in time.
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. 2023 · 2023
Closest in time.
How to Index Item IDs for Recommendation Foundation Models
Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Sequential Recommendation with Self-Attentive Multi-Adversarial Network. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020 . ACM, 89–98
Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin Ding, and Ji-Rong Wen. 2020 · 2020
Cited alongside, same era.
U-BERT: Pre-training User Representations for Improved Recommendation. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021 . AAAI Press, 4320–4327
Zhaopeng Qiu, Xian Wu, Jingyue Gao, and Wei Fan. 2021 · 2021
Cited alongside, same era.
Intent Contrastive Learning for Sequential Recommendation. In WWW ’22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022 . ACM, 2172–2182
Yongjun Chen, Zhiwei Liu, Jia Li, Julian J. McAuley, and Caiming Xiong. 2022 · 2022
Cited alongside, same era.
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). In RecSys ’22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18 - 23, 2022 . ACM, 299–315
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 . OpenReview.net
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.
Large Language Models are Zero-Shot Reasoners. In NeurIPS
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul, and Benjamin Bossan. 2022 · 2022
Cited alongside, same era.
Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation. In WSDM ’22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022 . ACM, 813–823
Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang. 2022 · 2022
Cited alongside, same era.
Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang. 2023 · 2023
Closest in time.
Platypus: Quick, Cheap, and Powerful Refinement of LLMs
Ariel N. Lee, Cole J. Hunter, and Nataniel Ruiz. 2023 · 2023
Closest in time.
Is ChatGPT a Good Recommender? A Preliminary Study
Junling Liu, Chao Liu, Renjie Lv, Kang Zhou, and Yan Zhang. 2023 · 2023
Closest in time.
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023 · 2023
Closest in time.
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
Closest in time.
LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
Closest in time.
ChatGPT: five priorities for research
Eva Anna Maria van Dis, Johan Bollen, Willem Zuidema, Robert van Rooij, and Claudi L H Bockting. 2023 · 2023
Closest in time.
Self-Instruct: Aligning Language Models with Self-Generated Instructions. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 . Association for Computational Linguistics, 13484–13508
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
Closest in time.
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, Hui Xiong, and Enhong Chen. 2023 · 2023
Closest in time.
OpenP5: Benchmarking Foundation Models for Recommendation
Shuyuan Xu, Wenyue Hua, and Yongfeng Zhang. 2023 · 2023
Closest in time.
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023 · 2023
Closest in time.
Equivariant Contrastive Learning for Sequential Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023 . ACM, 129–140
Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jaeboum Kim, Shoujin Wang, and Sunghun Kim. 2023 · 2023
Closest in time.
UserBERT: Pre-training User Model with Contrastive Self-supervision. In SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022 . ACM, 2087–2092
Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2022 · 2092
Closest in time.