Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System-Reference-Cited by-同舟云学术

Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System

Published:2020-09-22 Issue: Volume: Page:
ISSN:
Container-title:Fourteenth ACM Conference on Recommender Systems
language:
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Author:

Khenissi Sami¹,Mariem Boujelbene¹,Nasraoui Olfa¹

Affiliation:

1. University of Louisville, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3383313.3412260

Reference35 articles.

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

2. Himan Abdollahpouri Robin D. Burke and Bamshad Mobasher. 2019. Managing Popularity Bias in Recommender Systems with Personalized Re-ranking. ArXiv abs/1901.07555(2019). Himan Abdollahpouri Robin D. Burke and Bamshad Mobasher. 2019. Managing Popularity Bias in Recommender Systems with Personalized Re-ranking. ArXiv abs/1901.07555(2019).

3. Himan Abdollahpouri Masoud Mansoury Robin Burke and Bamshad Mobasher. 2019. The Unfairness of Popularity Bias in Recommendation. Himan Abdollahpouri Masoud Mansoury Robin Burke and Bamshad Mobasher. 2019. The Unfairness of Popularity Bias in Recommendation.

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