Collaborative fair-is-better filtering for implicit feedback
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Published Version
Date
2024
Authors
Dong, Hoang V.
Nguyen, Huu-Quang
Nguyen, Hoang D.
Le, Duc-Trong
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Published Version
Abstract
With the wide adoption of recommender systems, fairness has increasingly become a critical topic in many applications, such as e-commerce, job search, and online entertainment. Collaborative filtering is susceptible to unfair recommendations for users from sensitive groups due to the non-negligible presence of biases. Whereas recent work mostly concerned fairness with the explicit ratings, we propose fairness-awareness recommendation models, referred to as CFBF, for implicit feedback (e.g., clicks, views, or purchases), which are ubiquitous in today’s context. This paper considers sensitive attributes, such as gender and age, in both disjoined and combined manners to investigate the models’ unfairness. We discuss several fairness metrics for implicit feedback recommendations based on Spearman’s rank correlation and Kendal Tau. Comprehensive experiments on Movielens and LastFM show that CFBF significantly improves user groups’ fairness with comparable and even better ranking performance.
Description
Keywords
Collaborative filtering , Fairness , Implicit feedback
Citation
Dong, H. V., Nguyen, H.-Q., Nguyen, H. D. and Le, D.-T. (2024) ‘Collaborative fair-is-better filtering for implicit feedback’, 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2024), Seville, Spain, 11-13 September. Procedia Computer Science, 246, pp. 1498-1507. https://doi.org/10.1016/j.procs.2024.09.599