Argumentative explanations for recommendations - Effect of display style and profile transparency

Hernandez-Bocanegra, D. C., & Ziegler, J. (2020). Proceedings of the Mensch Und Computer 2020 Workshop on User-Centered Artificial Intelligence (UCAI 2020).

Abstract

Providing explanations based on user reviews in recommender systems may increase users’ perception of transparency. However, little is known about how these explanations should be presented to users in order to increase both their understanding and acceptance. We present in this paper a user study to investigate the effect of different display styles (visual and text only) on the perception of review-based explanations for recommended hotels. Additionally, we also aim to test the differences in users’ perception when providing information about their own profiles, in addition to a summarized view on the opinions of other users about the recommended hotel. Our results suggest that the perception of explanations regarding these aspects may vary depending on user characteristics, such as decision-making styles or social awareness.

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