Explanations help users to better understand why a set of items has been recommended. Compared to single user recommender systems, explanations in group recommender systems have further goals. Examples thereof are fairness which helps to take into account as much as possible group members' preferences and consensus which persuades group members to agree on a decision. This paper proposes different explanation types and investigates which explanation best helps to increase the fairness perception, consensus perception, and satisfaction of group members with regard to group recommendations. We conducted a user study to evaluate the proposed explanations. The results show that explanations which take into account preferences of all or the majority of group members achieve the best results in terms of the mentioned aspects. Moreover, there exist positive correlations among these aspects, i.e., as the perceived fairness (or the perceived consensus) of explanations increases, so does the satisfaction of users with regard to group recommendations. In addition, in the context of repeated decisions, the inclusion of group members' satisfaction from previous decisions in the explanations helps to improve the fairness perception of users with regard to group recommendations.
|Titel||UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization|
|Publikationsstatus||Veröffentlicht - 2019|
|Veranstaltung||27th ACM Conference on User Modeling, Adaptation and Personalization - Larnaca, Zypern|
Dauer: 9 Jun 2019 → 12 Jun 2019
|Konferenz||27th ACM Conference on User Modeling, Adaptation and Personalization|
|Zeitraum||9/06/19 → 12/06/19|