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dc.contributor.authorEkstrand, Michael D. ( Orcid Icon 0000-0003-2467-0108 )
dc.contributor.authorHarper, F. Maxwell ( )
dc.contributor.authorWillemsen, Martijn C. ( )
dc.contributor.authorKonstan, Joseph A. ( Orcid Icon 0000-0002-7788-2748 )
dc.date.accessioned2014-10-14T15:25:19Z
dc.date.available2014-10-14T15:25:19Z
dc.date.issued2014-10
dc.identifier.citationEkstrand, M. D., Harper, F. M., Willemsen, M. C., & Konstan, J. A. (2014). User perception of differences in recommender algorithms. Proceedings of the 8th ACM Conference on Recommender Systems, pp. 161-168.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/5321
dc.description.abstractRecent 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.en_US
dc.description.sponsorshipNSF IIS 08-08692, IIS 10-17697.en_US
dc.formatText
dc.format.extent8 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.publisherAssociation for Computing Machineryen_US
dc.sourceProceedings of the Eighth ACM Conference on Recommender Systems, 2014, Silicon Valley, California, United States
dc.subjectRecommender systemsen_US
dc.subjectHuman-computer interactionen_US
dc.subjectUser studyen_US
dc.titleUser Perception of Differences in Recommender Algorithmsen_US
dc.typeacceptedVersion
txstate.documenttypeArticle
dc.identifier.doihttp://dx.doi.org/10.1145/2645710.2645737
txstate.departmentComputer Science


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