Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?-Reference-Cited by-同舟云学术

Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?

Published:2021-09-13 Issue: Volume: Page:
ISSN:
Container-title:Fifteenth ACM Conference on Recommender Systems
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Author:

Lesota Oleg¹,Melchiorre Alessandro²,Rekabsaz Navid³,Brandl Stefan⁴,Kowald Dominik⁵,Lex Elisabeth⁵,Schedl Markus³

Affiliation:

1. Johannes Kepler University and Linz Institute of Technology, Austria

2. Linz Institute of Technology, Austria

3. Johannes Kepler University Linz and Linz Institute of Technology, Austria

4. Johannes Kepler University Linz, Austria

5. Graz University of Technology, Austria

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3460231.3478843

Reference20 articles.

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

2. Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In The thirty-second international flairs conference. Himan Abdollahpouri Robin Burke and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In The thirty-second international flairs conference.

3. Connecting user and item perspectives in popularity debiasing for collaborative recommendation

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