Conference proceeding
User Perception of Differences in Recommender Algorithms
PROCEEDINGS OF THE 8TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'14), pp 161-168
01 Jan 2014
Abstract
Recent developments in user evaluation of recommender systems have brought forth powerful new tools for understanding what makes recommendations effective and useful. We apply these methods to understand how users evaluate recommendation lists for the purpose of selecting an algorithm for finding movies. This paper reports on an experiment in which we asked users to compare lists produced by three common collaborative filtering algorithms on the dimensions of novelty, diversity, accuracy, satisfaction, and degree of personalization, and to select a recommender that they would like to use in the future. We find that satisfaction is negatively dependent on novelty and positively dependent on diversity in this setting, and that satisfaction predicts the user's final selection. We also compare users' subjective perceptions of recommendation properties with objective measures of those same characteristics. To our knowledge, this is the first study that applies modern survey design and analysis techniques to a within-subjects, direct comparison study of recommender algorithms.
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Details
- Title
- User Perception of Differences in Recommender Algorithms
- Creators
- Michael D. Ekstrand - Texas State UniversityF. Maxwell Harper - University of MinnesotaMartijn C. Willemsen - Eindhoven University of TechnologyJoseph A. Konstan - University of MinnesotaACM
- Publication Details
- PROCEEDINGS OF THE 8TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'14), pp 161-168
- Publisher
- Assoc Computing Machinery
- Number of pages
- 8
- Grant note
- 1017697 / Direct For Computer & Info Scie & Enginr; Div Of Information & Intelligent Systems; National Science Foundation (NSF); NSF - Directorate for Computer & Information Science & Engineering (CISE) IIS 08-08692; 10-17697 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000569706700021
- Scopus ID
- 2-s2.0-84908878885
- Other Identifier
- 991021818497804721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods