Collaborative filtering algorithms are prone to mainstream-taste bias

Pantelis Pipergias Analytis*, Philipp Hager*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Collaborative filtering has been a dominant approach in the recommender systems community since the early 1990s. Collaborative filtering (and other) algorithms, however, have been predominantly evaluated by aggregating results across users or user groups. These performance averages hide large disparities: an algorithm may perform very well for some users (or groups) and poorly for others. We show that performance variation is large and systematic. In experiments on three large-scale datasets and using an array of collaborative filtering algorithms, we demonstrate large performance disparities across algorithms, datasets and metrics for different users. We then show that two key features that characterize users, their mean taste similarity and dispersion in taste similarity with other users, can systematically explain performance variation better than previously identified features. We use these two features to visualize algorithm performance for different users and we point out that this mapping can capture different categories of users that have been proposed before. Our results demonstrate an extensive mainstream-taste bias in collaborative filtering algorithms, which implies a fundamental fairness limitation that needs to be mitigated.

Original languageEnglish
Title of host publicationRecSys '23 : Proceedings of the 17th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery
Publication date14. Sept 2023
Pages750-756
ISBN (Electronic)9798400702419
DOIs
Publication statusPublished - 14. Sept 2023
Event17th ACM Conference on Recommender Systems, RecSys 2023 - Singapore, Singapore
Duration: 18. Sept 202322. Sept 2023

Conference

Conference17th ACM Conference on Recommender Systems, RecSys 2023
Country/TerritorySingapore
CitySingapore
Period18/09/202322/09/2023

Keywords

  • algorithmic fairness
  • performance variation
  • user features

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