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Traditional recommender systems leverage users' item preference history to recommend novel content that users may like.
Good Intentions, Bad Habits, and Effects of Forming Implementation Intentions on Healthy Eating
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Language Models are Few-Shot Learners
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 · 2005
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Collaborative Filtering for Implicit Feedback Datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (ICDM ’08) . 263–272
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RecoBERT: A Catalog Language Model for Text-Based Recommendations
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MyMediaLite: A Free Recommender System Library. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys ’11) . 305–308
Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011 · 2011
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Content-based Recommender Systems: State of the Art and Trends
Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2011 · 2011
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SLIM: Sparse Linear Methods for Top-N Recommender Systems. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM ’11) . 497–506
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Initial Profile Generation in Recommender Systems Using Pairwise Comparison
Lior Rokach and Slava Kisilevich. 2012 · 2012
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Interview Process Learning for Top-N Recommendation. In Proceedings of the ACM Conference on Recommender Systems (RecSys ’13) . 331–334
Fangwei Hu and Yong Yu. 2013 · 2013
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The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan. 2015 · 2015
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Towards Conversational Recommender Systems. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16) . 815–824
Konstantina Christakopoulou, Filip Radlinski, and Katja Hofmann. 2016 · 2016
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Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems
Marius Kaminskas and Derek Bridge. 2016 · 2016
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Defining and Supporting Narrative-Driven Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17) . 238–242
Toine Bogers and Marijn Koolen. 2017 · 2017
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Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW ’17) . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
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Understanding How People Use Natural Language to Ask for Recommendations. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17) . 229–237
Jie Kang, Kyle Condiff, Shuo Chang, Joseph A. Konstan, Loren Terveen, and F. Maxwell Harper. 2017 · 2017
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Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 World Wide Web Conference (WWW ’18) . 689–698
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018 · 2018
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Preference Elicitation as an Optimization Problem. In Proceedings of the ACM Conference on Recommender Systems (RecSys ’18) . 172–180
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Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, and Ilya Eckstein. 2021 · 2021
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Denoising Self-Attentive Sequential Recommendation. In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys ’22) . 92–101
Huiyuan Chen, Yusan Lin, Menghai Pan, Lan Wang, Chin-Chia Michael Yeh, Xiaoting Li, Yan Zheng, Fei Wang, and Hao Yang. 2022 · 2022
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PaLM: Scaling Language Modeling with Pathways
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Transparent, Scrutable and Explainable User Models for Personalized Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’19) . 265–274
Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019 · 2019
Cited alongside, same era.
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys ’19) . 101–109
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019 · 2019
Cited alongside, same era.
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, Volume 1 (Long and Short Papers) (NAACL ’19) . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Embarrassingly Shallow Autoencoders for Sparse Data. In The World Wide Web Conference (WWW ’19) . 3251–3257
Harald Steck. 2019 · 2019
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What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20) . 388–397
Gustavo Penha and Claudia Hauff. 2020 · 2020
Cited alongside, same era.
Program Synthesis with Large Language Models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. 2021 · 2021
Cited alongside, same era.
On Interpretation and Measurement of Soft Attributes for Recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21) . 890–899
Krisztian Balog, Filip Radlinski, and Alexandros Karatzoglou. 2021 · 2021
Cited alongside, same era.
Advances and Challenges in Conversational Recommender Systems: A Survey
Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys ’22) . 299–315
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
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Towards Universal Sequence Representation Learning for Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’22) . 585–593
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. 2022 · 2022
Later among the works it cites.
Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations. In Proceedings of the ACM Web Conference 2022 (WWW ’22) . 2433–2441
Zahra Nazari, Praveen Chandar, Ghazal Fazelnia, Catherine M. Edwards, Benjamin Carterette, and Mounia Lalmas. 2022 · 2022
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Don’t Recommend the Obvious: Estimate Probability Ratios. In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys ’22) . 188–197
Roberto Pellegrini, Wenjie Zhao, and Iain Murray. 2022 · 2022
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On Natural Language User Profiles for Transparent and Scrutable Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22) . 2863–2874
Filip Radlinski, Krisztian Balog, Fernando Diaz, Lucas Dixon, and Ben Wedin. 2022 · 2022
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Finetuned Language Models Are Zero-Shot Learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022 · 2022
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Language Models are Realistic Tabular Data Generators
Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci. 2023 · 2023
Closest in time.
Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23) . 2754–2764
Arun Tejasvi Chaganty, Megan Leszczynski, Shu Zhang, Ravi Ganti, Krisztian Balog, and Filip Radlinski. 2023 · 2023
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Leveraging Large Language Models in Conversational Recommender Systems
Luke Friedman, Sameer Ahuja, David Allen, Zhenning Tan, Hakim Sidahmed, Changbo Long, Jun Xie, Gabriel Schubiner, Ajay Patel, Harsh Lara, Brian Chu, Zexi Chen, and Manoj Tiwari. 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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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
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