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Recommendation algorithms have been pivotal in handling the overwhelming volume of online content.
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.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 1904
Earlier work this paper cites.
An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval . 230–237
Jonathan L Herlocker, Joseph A Konstan, Al Borchers, and John Riedl. 1999 · 1999
Earlier work this paper cites.
Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web . 285–295
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
Earlier work this paper cites.
A personalized system for conversational recommendations
Cynthia A Thompson, Mehmet H Goker, and Pat Langley. 2004 · 2004
Earlier work this paper cites.
Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. 2005 · 2005
Earlier work this paper cites.
A normalized Levenshtein distance metric
Li Yujian and Liu Bo. 2007 · 2007
Earlier work this paper cites.
Introducing serendipity in a content-based recommender system. In 2008 eighth international conference on hybrid intelligent systems . IEEE, 168–173
Leo Iaquinta, Marco De Gemmis, Pasquale Lops, Giovanni Semeraro, Michele Filannino, and Piero Molino. 2008 · 2008
Earlier work this paper cites.
Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011). In Proceedings of the fifth ACM conference on Recommender systems . 387–388
Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2011 · 2011
Earlier work this paper cites.
Web data mining: exploring hyperlinks, contents, and usage data . Vol. 1
Bing Liu et al · 2011
Earlier work this paper cites.
The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
Cited alongside, same era.
Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems
Marius Kaminskas and Derek Bridge. 2016 · 2016
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.
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.
Actionable Recourse in Linear Classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency . ACM
Berk Ustun, Alexander Spangher, and Yang Liu. 2019 · 2019
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Later among the works it cites.
The unreliability of explanations in few-shot in-context learning
Xi Ye and Greg Durrett. 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 Zhang Jiawei. 2023a · 2023
Later among the works it cites.
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Language models as recommender systems: Evaluations and limitations
Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, and Hao Wang. 2021 · 2021
Cited alongside, same era.
Complexity-based prompting for multi-step reasoning
Yao Fu, Hao Peng, Ashish Sabharwal, Peter Clark, and Tushar Khot. 2022 · 2022
Cited alongside, same era.
Intra-list similarity and human diversity perceptions of recommendations: the details matter
Mathias Jesse, Christine Bauer, and Dietmar Jannach. 2022 · 2022
Cited alongside, same era.
A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera. 2022 · 2022
Cited alongside, same era.
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023b · 2023
Later among the works it cites.
IMDb Top 250
IMDb. 2023 · 2023
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Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023 · 2023
Later among the works it cites.
Is chatgpt a good recommender? a preliminary study
Junling Liu, Chao Liu, Renjie Lv, Kang Zhou, and Yan Zhang. 2023 · 2023
Later among the works it cites.
Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, and Lucas Dixon. 2023 · 2023
Later among the works it cites.