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The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM).
The Unfairness of Popularity Bias in Recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019 · 1907
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Surveying Multiple Sensitive Attributes using an Extension of the Randomized-Response Technique
Morten Moshagen and Jochen Musch. 2011 · 2011
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Predicting data that people refuse to disclose; how data mining predictions challenge informational self-determination
BHM Custers. 2012 · 2012
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Social recommendation: a review
Jiliang Tang, Xia Hu, and Huan Liu. 2013 · 2013
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Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity. In Proceedings of the 23rd International Conference on World Wide Web (WWW ’14) . Association for Computing Machinery, 677–686
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HCI for Recommender Systems: The Past, the Present and the Future. In Proceedings of the 10th ACM Conference on Recommender Systems . Association for Computing Machinery, 123–126
André Calero Valdez, Martina Ziefle, and Katrien Verbert. 2016 · 2016
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Controlling Popularity Bias in Learning-to-Rank Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17) . Association for Computing Machinery, 42–46
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017 · 2017
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Equity of attention: Amortizing individual fairness in rankings. In The 41st international acm sigir conference on research & development in information retrieval . 405–414
Asia J Biega, Krishna P Gummadi, and Gerhard Weikum. 2018 · 2018
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Fairness in Recommendation Ranking through Pairwise Comparisons. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019 . ACM, 2212–2220
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, and Cristos Goodrow. 2019 · 2019
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RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2020 . 3356–3369
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020 · 2020
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Social Biases in NLP Models as Barriers for Persons with Disabilities. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 5491–5501
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl. 2020 · 2020
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CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Association for Computational Linguistics, Online
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020 · 2020
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On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms
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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
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Large language models associate Muslims with violence
Abubakar Abid, Maheen Farooqi, and James Zou. 2021 · 2021
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User-Oriented Fairness in Recommendation. In Proceedings of the Web Conference 2021 (WWW ’21) . Association for Computing Machinery, 624–632
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021a · 2021
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An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes. In Proceedings of the 15th ACM Conference on Recommender Systems (RecSys ’21) . Association for Computing Machinery, 1–11
Matus Tomlein, Branislav Pecher, Jakub Simko, Ivan Srba, Robert Moro, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, and Maria Bielikova. 2021 · 2021
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Practical compositional fairness: Understanding fairness in multi-component recommender systems. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 436–444
Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H Chi, Jilin Chen, and Alex Beutel. 2021 · 2021
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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 F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Experiments on generalizability of user-oriented fairness in recommender systems. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2755–2764
Hossein A Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, and Mohammad Aliannejadi. 2022 · 2022
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OPT: Open Pre-trained Transformer Language Models
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Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
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Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, and James Caverlee. 2021 · 2021
Cited alongside, same era.
ChatGPT: Fundamentals, Applications and Social Impacts. In 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS) . 1–8
Malak Abdullah, Alia Madain, and Yaser Jararweh. 2022 · 2022
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Constitutional AI: Harmlessness from AI Feedback
Yuntao Bai et al · 2022
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PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery et al. 2022 · 2022
Cited alongside, same era.
Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al · 2022
Cited alongside, same era.
Toward Pareto efficient fairness-utility trade-off in recommendation through reinforcement learning. In Proceedings of the fifteenth ACM international conference on web search and data mining . 316–324
Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, and Yongfeng Zhang. 2022 · 2022
Cited alongside, same era.
Data mining: concepts and techniques
Jiawei Han, Jian Pei, and Hanghang Tong. 2022 · 2022
Cited alongside, same era.
Fairness in Recommendation: A Survey
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2022 · 2022
Cited alongside, same era.
Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, et al · 2023
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TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys ’23) . Association for Computing Machinery
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
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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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LLaMA: Open and Efficient Foundation Language Models
et al. Hugo Touvron. 2023 · 2023
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ChatGPT: A Meta-Analysis after 2.5 Months
Christoph Leiter, Ran Zhang, Yanran Chen, Jonas Belouadi, Daniil Larionov, Vivian Fresen, and Steffen Eger. 2023 · 2023
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Ruyu Li, Wenhao Deng, Yu Cheng, Zheng Yuan, Jiaqi Zhang, and Fajie Yuan. 2023 · 2023
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Generative Recommendation: Towards Next-generation Recommender Paradigm
Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, and Tat-Seng Chua. 2023a · 2023
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A Survey on the Fairness of Recommender Systems
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023b · 2023
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Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023 , Hsin-Hsi Chen, Wei-Jou (Edward) Duh, Hen-Hsen Huang, Makoto P. Kato, Josiane Mothe, and Barbara Poblete (Eds.). ACM, 2639–2649
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023 · 2023
Closest in time.
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023 · 2023
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