Hernandez-Bocanegra, Diana Carolina; Donkers, Tim; Ziegler, Jürgen:
Effects of Argumentative Explanation Types on the Perception of Review-Based Recommendations
In: UMAP 2020 Adjunct : Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization - UMAP 2020, 14 - 17 July 2020, Genoa, Italy - New York: Association for Computing Machinery (ACM), 2020, pp. 219 - 225
2020book article/chapter in ProceedingsOA Gold
Computer ScienceFaculty of Engineering » Computer Science and Applied Cognitive Science » Computer Science » Interactive Systems
Title in English:
Effects of Argumentative Explanation Types on the Perception of Review-Based Recommendations
Hernandez-Bocanegra, Diana CarolinaORCID iDSCOPUSLSF; Donkers, TimSCOPUSLSF; Ziegler, JürgenSCOPUSLSF
Open Access?:
OA Gold
Scopus ID:
Language of text:
Keyword, Topic:
explanations ; recommender systems ; user study

Abstract in English:

Recommender systems have achieved considerable maturity and accuracy in recent years. However, the rationale behind recommendations mostly remains opaque. Providing textual explanations based on user reviews may increase users' perception of transparency and, by that, overall system satisfaction. However, little is known about how these explanations can be effectively and efficiently presented to the user. In the following paper, we present an empirical study conducted in the domain of hotels to investigate the effect of different textual explanation types on, among others, perceived system transparency and trustworthiness, as well as the overall assessment of explanation quality. The explanations presented to participants follow an argument-based design, which we propose to provide a rationale to support a recommendation in a structured way. Our results show that people prefer explanations that include an aggregation using percentages of other users' opinions, over explanations that only include a brief summary of opinions. The results additionally indicate that user characteristics such as social awareness may influence the perception of explanation quality.