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Recent state-of-the-art recommender systems predominantly rely on either implicit or explicit feedback from users to suggest new items.
Language models are few-shot learners
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Probabilistic matrix factorization
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The quest for quality tags
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Matrix factorization techniques for recommender systems
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Do users rate or review? boost phrase-level sentiment labeling with review-level sentiment classification
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Using groups of items for preference elicitation in recommender systems
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The movielens datasets: History and context
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Adam: A method for stochastic optimization
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Explaining Recommendations: Design and Evaluation , 2 edition, pages 353–382. Springer US
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Transparent, scrutable and explainable user models for personalized recommendation
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022 · 2022
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Improving personalized explanation generation through visualization
Shijie Geng, Zuohui Fu, Yingqiang Ge, Lei Li, Gerard de Melo, and Yongfeng Zhang. 2022 · 2022
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Learning from sets of items in recommender systems
Mohit Sharma, F. Maxwell Harper, and George Karypis. 2019 · 2019
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Generate neural template explanations for recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2020 · 2020
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Explainable recommendation: A survey and new perspectives
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Personalized transformer for explainable recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2021 · 2021
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Personalized prompt learning for explainable recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2023a
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On natural language user profiles for transparent and scrutable recommendation
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Factual and informative review generation for explainable recommendation
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Bias and debias in recommender system: A survey and future directions
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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Quantifying the bias of transformer-based language models for african american english in masked language modeling
Flavia Salutari, Jerome Ramos, Hossein A Rahmani, Leonardo Linguaglossa, and Aldo Lipani. 2023 · 2023
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Large language models are competitive near cold-start recommenders for language-and item-based preferences
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Llama 2: Open foundation and fine-tuned chat models
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