Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation-Reference-Cited by-同舟云学术

Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation

Published:2023-09-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM Conference on Recommender Systems
language:
Short-container-title:

Author:

Zhang Jizhi¹^ORCID,Bao Keqin²^ORCID,Zhang Yang³^ORCID,Wang Wenjie⁴^ORCID,Feng Fuli⁵^ORCID,He Xiangnan⁵^ORCID

Affiliation:

1. School of Data Science, University of Science and Technology of China, China

2. Cyber Science and Techonology, University of Science and Technology of China, China

3. School of Information Science and Technology, University of Science and Technology of China, China

4. School of Computing, National University of Singapore, Singapore

5. University of Science and Technology of China, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604915.3608860

Reference42 articles.

1. Controlling Popularity Bias in Learning-to-Rank Recommendation

2. Himan Abdollahpouri , Masoud Mansoury , Robin Burke , and Bamshad Mobasher . 2019. The Unfairness of Popularity Bias in Recommendation. CoRR abs/1907.13286 ( 2019 ). http://arxiv.org/abs/1907.13286 Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The Unfairness of Popularity Bias in Recommendation. CoRR abs/1907.13286 (2019). http://arxiv.org/abs/1907.13286

3. Malak Abdullah , Alia Madain , and Yaser Jararweh . 2022 . 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. ChatGPT: Fundamentals, Applications and Social Impacts. In 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS). 1–8.

4. Large language models associate Muslims with violence

5. Qingyao Ai , Ting Bai , Zhao Cao , Yi Chang , Jiawei Chen , Zhumin Chen , Zhiyong Cheng , Shoubin Dong , Zhicheng Dou , Fuli Feng , 2023. Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community. arXiv preprint arXiv:2307.09751 ( 2023 ). Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, 2023. Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community. arXiv preprint arXiv:2307.09751 (2023).

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency;ACM Transactions on Recommender Systems;2024-08-28

2. A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys);Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

3. Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

4. A survey on large language models for recommendation;World Wide Web;2024-08-22

5. Fairness in machine learning: definition, testing, debugging, and application;Science China Information Sciences;2024-08-15