Kunkel, Johannes; Donkers, Tim; Barbu, Catalin-Mihai; Ziegler, Jürgen:
Trust-Related Effects of Expertise and Similarity Cues in Human-Generated Recommendations
In: Joint Proceedings of the ACM IUI 2018 Workshops / Said, Alan; Komatsu, Takanori (Hrsg.). - Workshops on the 23rd ACM Conference on Intelligent User Interfaces (ACM IUI 2018), 11 March 2018, Tokyo, Japan - Aachen: RWTH Aachen, 2018 - (CEUR Workshop Proceedings ; 2068)
2018Buchaufsatz/Kapitel in TagungsbandOA Gold
InformatikFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Titel in Englisch:
Trust-Related Effects of Expertise and Similarity Cues in Human-Generated Recommendations
Autor*in:
Kunkel, JohannesUDE
LSF ID
57574
Sonstiges
der Hochschule zugeordnete*r Autor*in
;
Donkers, TimUDE
GND
1318565251
LSF ID
59377
ORCID
0000-0002-9230-1243ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
;
Barbu, Catalin-MihaiUDE
LSF ID
58102
Sonstiges
der Hochschule zugeordnete*r Autor*in
;
Ziegler, JürgenUDE
GND
1015876811
GND
1077664516
LSF ID
3881
ORCID
0000-0001-9603-5272ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
Open Access?:
OA Gold
Notiz:
OA platinum
Sprache des Textes:
Englisch

Abstract in Englisch:

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.