The effects of transparency on perceived and actual competence of a content-based recommender

H.S.M. Cramer, B.J. Wielinga, S. Ramlal, V. Evers, M.W. Someren, van, L.W. Rutledge, N. Stash

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

    3 Citations (Scopus)
    3 Downloads (Pure)


    Perceptions of a system's competence influence acceptance of that system [31]. Ideally, users' perception of competence matches the actual competence of a system. This paper investigates the relation between actual and perceived competence of transparent Semantic Web recommender systems that explain recommendations in terms of shared item concepts. We report an experiment comparing non-transparent and transparent versions of a content-based recommender. Results indicate that in the transparent condition, perceived competence and actual competence (in specific recall) were related, while in the non-transparent condition they were not. Providing insight in what aspects of items triggered their recommendation, by showing the concepts that were the basis for a recommendation, gave users a better assessment of how well the system worked. Keywords: Actual competence; Explanations; Perceived competence; Recommender systems; Transparency
    Original languageEnglish
    Title of host publicationExploring HCI Challenges (5th International Workshop on Semantic Web User Interaction, SWUI 2008, collocated with the Computer Human Interaction Conference, CHI 2008, Florence, Italy, April 5, 2008)
    EditorsD. Degler, M.C. Schraefel, J. Golbeck, A. Bernstein, L. Rutledge
    Place of PublicationAachen
    Publication statusPublished - 2009

    Publication series

    NameCEUR Workshop Proceedings
    ISSN (Print)1613-0073


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