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.
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RWTH Aachen
CEUR Workshop Proceedings
Cultural Heritage Information Personalization
International Semantic Web User Interaction Workshop
Human-Centered Data Analytics

Cramer, H, Wielinga, B, Ramlal, S, Evers, V, Rutledge, L, & Stash, N. (2008). The effects of transparency on perceived and actual competence of a content-based recommender. In Proceedings of Semantic Web User Interaction workshop at CHI2008 (SWUI2008). RWTH Aachen.