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Item Familiarity as a Possible Confounding Factor in User-Centric Recommender Systems Evaluation

Item Familiarity as a Possible Confounding Factor in User-Centric Recommender Systems Evaluation AbstractUser studies play an important role in academic research in the field of recommender systems as they allow us to assess quality factors other than the predictive accuracy of the underlying algorithms. User satisfaction is one such factor that is often evaluated in laboratory settings and in many experimental designs one task of the participants is to assess the suitability of the system-generated recommendations. The effort required by the user to make such an assessment can, however, depend on the user’s familiarity with the presented items and directly impact on the reported user satisfaction. In this paper, we report the results of a preliminary recommender systems user study using Mechanical Turk, which indicates that item familiarity is strongly correlated with overall satisfaction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png i-com de Gruyter

Item Familiarity as a Possible Confounding Factor in User-Centric Recommender Systems Evaluation

i-com , Volume 14 (1): 11 – Apr 15, 2015

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Publisher
de Gruyter
Copyright
© 2015 Walter de Gruyter GmbH, Berlin/Boston
ISSN
2196-6826
eISSN
2196-6826
DOI
10.1515/icom-2015-0018
Publisher site
See Article on Publisher Site

Abstract

AbstractUser studies play an important role in academic research in the field of recommender systems as they allow us to assess quality factors other than the predictive accuracy of the underlying algorithms. User satisfaction is one such factor that is often evaluated in laboratory settings and in many experimental designs one task of the participants is to assess the suitability of the system-generated recommendations. The effort required by the user to make such an assessment can, however, depend on the user’s familiarity with the presented items and directly impact on the reported user satisfaction. In this paper, we report the results of a preliminary recommender systems user study using Mechanical Turk, which indicates that item familiarity is strongly correlated with overall satisfaction.

Journal

i-comde Gruyter

Published: Apr 15, 2015

Keywords: Recommender Systems; User Study; Satisfaction; Methodology

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