Robustness of privacy-preserving collaborative recommenders against popularity bias problem.

Autor:	Gulsoy M; Distance Education Research Center, Alaaddin Keykubat University, Antalya, Turkey.; Computer Engineering Department, Akdeniz University, Antalya, Turkey., Yalcin E; Computer Engineering Department, Sivas Cumhuriyet University, Sivas, Turkey., Bilge A; Computer Engineering Department, Akdeniz University, Antalya, Turkey.
Jazyk:	angličtina
Zdroj:	PeerJ. Computer science [PeerJ Comput Sci] 2023 Jul 06; Vol. 9, pp. e1438. Date of Electronic Publication: 2023 Jul 06 (Print Publication: 2023).
DOI:	10.7717/peerj-cs.1438
Abstrakt:	Recommender systems have become increasingly important in today's digital age, but they are not without their challenges. One of the most significant challenges is that users are not always willing to share their preferences due to privacy concerns, yet they still require decent recommendations. Privacy-preserving collaborative recommenders remedy such concerns by letting users set their privacy preferences before submitting to the recommendation provider. Another recently discussed challenge is the problem of popularity bias, where the system tends to recommend popular items more often than less popular ones, limiting the diversity of recommendations and preventing users from discovering new and interesting items. In this article, we comprehensively analyze the randomized perturbation-based data disguising procedure of privacy-preserving collaborative recommender algorithms against the popularity bias problem. For this purpose, we construct user personas of varying privacy protection levels and scrutinize the performance of ten recommendation algorithms on these user personas regarding the accuracy and beyond-accuracy perspectives. We also investigate how well-known popularity-debiasing strategies combat the issue in privacy-preserving environments. In experiments, we employ three well-known real-world datasets. The key findings of our analysis reveal that privacy-sensitive users receive unbiased and fairer recommendations that are qualified in diversity, novelty, and catalogue coverage perspectives in exchange for tolerable sacrifice from accuracy. Also, prominent popularity-debiasing strategies fall considerably short as provided privacy level improves. Competing Interests: The authors declare there are no competing interests. (©2023 Gulsoy et al.)
Databáze:	MEDLINE
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