Letting users choose recommender algorithms: An experimental study

Michael D. Ekstrand, Daniel Kluver, Max Harper, Joseph A Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Scopus citations

Abstract

Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to understand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior. We found that a substantial portion of our user base (25%) used the recommender-switching feature. The majority of users who used the control only switched algorithms a few times, trying a few out and settling down on an algorithm that they would leave alone. The largest number of users prefer a matrix factorization algorithm, followed closely by item-item collaborative filtering; users selected both of these algorithms much more often than they chose a non-personalized mean recommender. The algorithms did produce measurably different recommender lists for the users in the study, but these differences were not directly predictive of user choice.

Original languageEnglish (US)
Title of host publicationRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages11-18
Number of pages8
ISBN (Electronic)9781450336925
DOIs
StatePublished - Sep 16 2015
Event9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria
Duration: Sep 16 2015Sep 20 2015

Publication series

NameRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems

Other

Other9th ACM Conference on Recommender Systems, RecSys 2015
CountryAustria
CityVienna
Period9/16/159/20/15

Keywords

  • Experiment
  • Field study
  • Recommender systems

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    Ekstrand, M. D., Kluver, D., Harper, M., & Konstan, J. A. (2015). Letting users choose recommender algorithms: An experimental study. In RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems (pp. 11-18). (RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/2792838.2800195