Debiasing Recommendation with Personal Popularity-Reference-Cited by-同舟云学术

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Debiasing Recommendation with Personal Popularity

Published:2024-05-13 Issue: Volume:1247 Page:3400-3409
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Ning Wentao¹^ORCID,Cheng Reynold²^ORCID,Yan Xiao³^ORCID,Kao Ben²^ORCID,Huo Nan²^ORCID,Haldar Nur Al Hasan⁴^ORCID,Tang Bo⁵^ORCID

Affiliation:

1. The University of Hong Kong & Southern University of Science and Technology, Hong Kong, Hong Kong

2. The University of Hong Kong, Hong Kong, Hong Kong

3. Southern University of Science and Technology, Shenzhen, Hong Kong

4. Curtin University, Curtin, Australia

5. Southern University of Science and Technology, Shenzhen, China

Funder

the University of Hong Kong

the HKU Outstanding Research Student Supervisor Award 2022-23

Shenzhen Fundamental Research Program

the France/Hong Kong Joint Research Scheme 2020/21

the Guangdong Provincial Key Laboratory

the Hong Kong Jockey Club Charities Trust

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645421

Reference60 articles.

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

2. H. Abdollahpouri, R. Burke, and B. Mobasher. Managing popularity bias in recommender systems with personalized re-ranking. In FLAIRS, pages 413--418, 2019.

3. The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation

4. User-centered Evaluation of Popularity Bias in Recommender Systems

5. Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations

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