Millecamp, Martijn; Verbert, Katrien; Naveed, Sidra; Ziegler, Jürgen:
To explain or not to explain : The effects of personal characteristics when explaining feature-based recommendations in different domains
In: IntRS 2019: Interfaces and Human Decision Making for Recommender Systems : Proceedings / Brusilovsky, Peter; de Gemmis, Marco; Felfernig, Alexander; Lops, Pasquale; O’Donovan, John; Semeraro, Giovanni; Willemsen, Martijn C. (Eds.). - 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS2019), 19.09.2019, Copenhagen - Aachen: RWTH Aachen, 2019 - (CEUR Workshop Proceedings ; 2450), pp. 10 - 18
2019book article/chapter in ProceedingsOA Gold
Mechanical EngineeringFaculty of Engineering » Computer Science and Applied Cognitive Science » Computer Science » Interactive Systems
Title in English:
To explain or not to explain : The effects of personal characteristics when explaining feature-based recommendations in different domains
Author:
Millecamp, MartijnSCOPUS; Verbert, KatrienSCOPUS; Naveed, SidraSCOPUS; Ziegler, JürgenSCOPUSLSF
Open Access?:
OA Gold
Scopus ID:
Note::
OA platinum
Language of text:
English
Keyword, Topic:
Evaluation ; Explanations ; Need for cognition ; Personal characteristics ; Recommender system ; User characteristics ; User modelling

Abstract in English:

In our daily life, we need to sift through various options which often results in choice overload. Recommender systems help to overcome this problem by suggesting potentially relevant items to the users. Explaining the relevancy of these items to users has become an increasingly important goal. In recent years, a large body of research has shown that explanations are an effective means for supporting decision-making processes. However, still little is known on how to best implement these explanations and how these explanations are perceived. In addition, it is unclear how this perception is affected by the product domain or by users’ personal characteristics. To fill these research gaps, we conducted an online user study (N=291) with different design mock-ups that represent explanations of feature-based recommendations in various recommendation scenarios in two product domains (music and camera) and using different recommendation techniques (content, collaborative, and hybrid). We conducted in each domain a between-subject study with a baseline without explanations and one of the three designs explaining the feature-based recommendations. The study offers empirical evidence on how the perception of feature-based explanations in various recommendation scenarios are moderated by both the product domains and personal characteristics of the user, in particular need for cognition.