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Recommendation systems help users find matched items based on their previous behaviors.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
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. 2019 · 1910
Earlier work this paper cites.
Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1929–1937
Xiaolei Wang, Kun Zhou, Ji-Rong Wen, and Wayne Xin Zhao. 2022 · 1937
Earlier work this paper cites.
A mathematical theory of communication
Claude Elwood Shannon. 1948 · 1948
Earlier work this paper cites.
Statistical inference for probabilistic functions of finite state Markov chains
Leonard E Baum and Ted Petrie. 1966 · 1966
Earlier work this paper cites.
The meaning and use of the area under a receiver operating characteristic (ROC) curve
James A Hanley and Barbara J McNeil. 1982 · 1982
Earlier work this paper cites.
Term-weighting approaches in automatic text retrieval
Gerard Salton and Christopher Buckley. 1988 · 1988
Earlier work this paper cites.
Getting to know you: learning new user preferences in recommender systems. In Proceedings of the 7th international conference on Intelligent user interfaces . 127–134
Al Mamunur Rashid, Istvan Albert, Dan Cosley, Shyong K Lam, Sean M McNee, Joseph A Konstan, and John Riedl. 2002 · 2002
Earlier work this paper cites.
Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web . 521–530
Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007 · 2007
Earlier work this paper cites.
Learning preferences of new users in recommender systems: an information theoretic approach
Al Mamunur Rashid, George Karypis, and John Riedl. 2008 · 2008
Earlier work this paper cites.
Weighted Content Based Methods for Recommending Connections in Online Social Networks
Ruth Garcia and Xavier Amatriain. 2010 · 2010
Earlier work this paper cites.
Multi-task feature learning for knowledge graph enhanced recommendation. In The world wide web conference . 2000–2010
Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019 · 2010
Earlier work this paper cites.
Effects of relevant contextual features in the performance of a restaurant recommender system
Blanca Vargas-Govea, Gabriel González-Serna, and Rafael Ponce-Medellın. 2011 · 2011
Earlier work this paper cites.
A collaborative filtering approach to mitigate the new user cold start problem
JesúS Bobadilla, Fernando Ortega, Antonio Hernando, and Jesús Bernal. 2012 · 2012
Earlier work this paper cites.
Survey of cold-start problem in collaborative filtering recommender system
Sun Dong-ting, He Tao, and Zhang Fu-hai. 2012 · 2012
Earlier work this paper cites.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
Earlier work this paper cites.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web . 507–517
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning . PMLR, 1126–1135
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
A meta-learning perspective on cold-start recommendations for items
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
A bayesian framework for learning rule sets for interpretable classification
Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, and Perry MacNeille. 2017 · 2017
Earlier work this paper cites.
Spatial-aware hierarchical collaborative deep learning for POI recommendation
Hongzhi Yin, Weiqing Wang, Hao Wang, Ling Chen, and Xiaofang Zhou. 2017 · 2017
Earlier work this paper cites.
Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, and Kun Gai. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Adafactor: Adaptive learning rates with sublinear memory cost. In International Conference on Machine Learning . PMLR, 4596–4604
Noam Shazeer and Mitchell Stern. 2018 · 2018
Cited alongside, same era.
Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM international conference on information and knowledge management . 417–426
Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018 · 2018
Cited alongside, same era.
Cold start in recommender systems—a survey from domain perspective
Rachna Sethi and Monica Mehrotra. 2021 · 2021
Later among the works it cites.
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021 · 2021
Later among the works it cites.
Language Models as Recommender Systems: Evaluations and Limitations. In I (Still) Can’t Believe It’s Not Better! NeurIPS 2021 Workshop
Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, and Hao Wang. 2021 · 2021
Later among the works it cites.
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022 · 2022
Later among the works it cites.
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Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 1059–1068
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
Cited alongside, same era.
Melu: Meta-learned user preference estimator for cold-start recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1073–1082
Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019 · 2019
Cited alongside, same era.
Recommender systems challenges and solutions survey. In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE) . IEEE, 149–155
Marwa Hussien Mohamed, Mohamed Helmy Khafagy, and Mohamed Hasan Ibrahim. 2019 · 2019
Cited alongside, same era.
An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation. In Proceedings of the 1st international workshop on deep learning practice for high-dimensional sparse data . 1–9
Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, and Jian Tang. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Well-read students learn better: On the importance of pre-training compact models
Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2019
Cited alongside, same era.
Deep learning for user interest and response prediction in online display advertising
Zhabiz Gharibshah, Xingquan Zhu, Arthur Hainline, and Michael Conway. 2020 · 2020
Cited alongside, same era.
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Later among the works it cites.
Personalized prompt learning for explainable recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2022 · 2022
Later among the works it cites.
In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
Later among the works it cites.
Zero-Shot Recommendation as Language Modeling. In Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II . Springer, 223–230
Damien Sileo, Wout Vossen, and Robbe Raymaekers. 2022 · 2022
Later among the works it cites.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
Later among the works it cites.
Self-Supervised Learning for Recommender Systems: A Survey
Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Jundong Li, and Zi Huang. 2022 · 2022
Later among the works it cites.
Subgraph retrieval enhanced model for multi-hop knowledge base question answering
Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, and Hong Chen. 2022 · 2022
Later among the works it cites.
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
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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. 2023 · 2023
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Large Language Models for Generative Recommendation: A Survey and Visionary Discussions
Lei Li, Yongfeng Zhang, Dugang Liu, and Li Chen. 2023 · 2023
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How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al · 2023
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Llmrec: Benchmarking large language models on recommendation task
Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, Kang Zhou, Yueqi Xie, Yuwei Cao, Shoujin Wang, Chenyu You, et al · 2023
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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. 2023b · 2023
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Generative Sequential Recommendation with GPTRec
Aleksandr V Petrov and Craig Macdonald. 2023 · 2023
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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, et al · 2023
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Recmind: Large language model powered agent for recommendation
Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, and Yingzhen Yang. 2023 · 2023
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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
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Facing the cold start problem in recommender systems
Blerina Lika, Kostas Kolomvatsos, and Stathes Hadjiefthymiades. 2014 · 2073
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