Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

Dominik Kowald*, Emanuel Lacic

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., Last.fm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.

Original languageEnglish
Title of host publicationAdvances in Bias and Fairness in Information Retrieval - 3rd International Workshop, BIAS 2022, Revised Selected Papers
Subtitle of host publicationThird International Workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, Revised Selected Papers
EditorsLudovico Boratto, Mirko Marras, Stefano Faralli, Giovanni Stilo
Pages1-11
Number of pages11
ISBN (Electronic)978-3-031-09316-6
DOIs
Publication statusPublished - 2022
Event44th European Conference on Information Retrieval: ECIR 2022 - Stavanger, Norway
Duration: 10 Apr 202214 Apr 2022

Publication series

NameCommunications in Computer and Information Science
Volume1610 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference44th European Conference on Information Retrieval
Abbreviated titleECIR 2022
Country/TerritoryNorway
CityStavanger
Period10/04/2214/04/22

Keywords

  • cs.IR
  • cs.AI
  • collaborative filtering
  • multimedia recommender systems
  • algorithmic fairness
  • popularity bias

ASJC Scopus subject areas

  • Mathematics(all)
  • Computer Science(all)

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