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 (Eds.). - 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)
2018book article/chapter in ProceedingsOA Gold
Computer ScienceFaculty of Engineering » Computer Science and Applied Cognitive Science » Computer Science » Interactive Systems
Title in English:
Trust-Related Effects of Expertise and Similarity Cues in Human-Generated Recommendations
Author:
Kunkel, JohannesUDE
LSF ID
57574
SCOPUS
57193770993
Other
connected with university
Donkers, TimUDE
LSF ID
59377
ORCID
0000-0002-9230-1243ORCID iD
SCOPUS
56904233500
Other
connected with university
Barbu, Catalin-MihaiUDE
LSF ID
58102
SCOPUS
56414525200
Other
connected with university
Ziegler, JürgenUDE
GND
1015876811
GND
1077664516
LSF ID
3881
ORCID
0000-0001-9603-5272ORCID iD
SCOPUS
12797843900
SCOPUS
57205710998
SCOPUS
57208443887
SCOPUS
58059170700
Other
connected with university
Open Access?:
OA Gold
Note:
OA platinum
Language of text:
English

Abstract in English:

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.