Affiliation:
1. University of Science and Technology of China, China
2. National University of Singapore, Singapore
Funder
USTC Research Funds of the Double First-Class Initiative
National Natural Science Foundation of China
National Key Research and Development Program of China
Reference35 articles.
1. Himan Abdollahpouri and Masoud Mansoury. 2020. Multi-sided exposure bias in recommendation. arXiv preprint arXiv:2006.15772(2020). Himan Abdollahpouri and Masoud Mansoury. 2020. Multi-sided exposure bias in recommendation. arXiv preprint arXiv:2006.15772(2020).
2. Hands on Data and Algorithmic Bias in Recommender Systems
3. Rocío Cañamares and Pablo Castells. 2018. Should i follow the crowd?: A probabilistic analysis of the effectiveness of popularity in recommender systems. In SIGIR. ACM 415–424. Rocío Cañamares and Pablo Castells. 2018. Should i follow the crowd?: A probabilistic analysis of the effectiveness of popularity in recommender systems. In SIGIR. ACM 415–424.
4. Jiawei Chen Hande Dong Yang Qiu Xiangnan He Xin Xin Liang Chen Guli Lin and Keping Yang. [n.d.]. AutoDebias: Learning to Debias for Recommendation. Jiawei Chen Hande Dong Yang Qiu Xiangnan He Xin Xin Liang Chen Guli Lin and Keping Yang. [n.d.]. AutoDebias: Learning to Debias for Recommendation.
5. Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2020. Bias and Debias in Recommender System: A Survey and Future Directions. arXiv preprint arXiv:2010.03240(2020). Jiawei Chen Hande Dong Xiang Wang Fuli Feng Meng Wang and Xiangnan He. 2020. Bias and Debias in Recommender System: A Survey and Future Directions. arXiv preprint arXiv:2010.03240(2020).
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