Trust-Related Effects of Expertise and Similarity Cues in Human-Generated Recommendations

Kunkel, J., Donkers, T., Barbu, C.-M., & Ziegler, J. (2018). In 2nd Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE), 11 March 2018, Tokyo, Japan.

Thesis by Jeanny Bormann

Abstract

A user’s trust in recommendations plays a central role in the acceptance or rejection of a recommendation. One factor that influences trust is the source of the recommendations. In this paper we describe an empirical study that investigates the trust-related influence of social presence arising in two scenarios: human-generated recommendations and automated recommending. We further compare visual cues indicating the expertise of a human recommendation source and its similarity with the target user, and evaluate their influence on trust. Our analysis indicates that even subtle visual cues can signal expertise and similarity effectively, thus influencing a user’s trust in recommendations. These findings suggest that automated recommender systems could benefit from the inclusion of social components–especially when conveying characteristics of the recommendation source. Thus, more informative and persuasive recommendation interfaces may be designed using such a mixed approach.

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