Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

12 Downloads (Pure)

Abstract

In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bias: the tendency of a recommender system to provide better recommendations to users who have a mainstream taste, as opposed to non-mainstream users. We propose NAECF, a conceptually simple but effective idea to address this bias. The idea consists of adding an autoencoder (AE) layer when learning user and item representations with text-based Convolutional Neural Networks. The AEs, one for the users and one for the items, serve as adversaries to the process of minimizing the rating prediction error when learning how to recommend. They enforce that the specific unique properties of all users and items are sufficiently well incorporated and preserved in the learned representations. These representations, extracted as the bottlenecks of the corresponding AEs, are expected to be less biased towards mainstream users, and to provide more balanced recommendation utility across all users. Our experimental results confirm these expectations, significantly improving the recommendations for nonmainstream users while maintaining the recommendation quality for mainstream users. Our results emphasize the importance of deploying extensive content-based features, such as online reviews, in order to better represent users and items to maximize the debiasing effect.
Original languageEnglish
Title of host publicationWSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery (ACM)
Pages103-111
Number of pages9
ISBN (Electronic)978-1-4503-8297-7
DOIs
Publication statusPublished - 2021
Event14th ACM International Conference on Web Search and Data Mining - Virtual Event, Israel
Duration: 8 Mar 202112 Mar 2021
Conference number: 14

Conference

Conference14th ACM International Conference on Web Search and Data Mining
Abbreviated titleWSDM '21
CountryIsrael
Period8/03/2112/03/21

Keywords

  • mainstream bias
  • recommender systems
  • user fairness

Fingerprint

Dive into the research topics of 'Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users'. Together they form a unique fingerprint.

Cite this