Naveed, Sidra; Loepp, Benedikt; Ziegler, Jürgen:
On the Use of Feature-based Collaborative Explanations : An Empirical Comparison of Explanation Styles
In: UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization - UMAP 2020; Genoa, Italy; 14 - 17 July 2020 - New York: Association for Computing Machinery (ACM), 2020, S. 226 - 232
2020Buchaufsatz/Kapitel in TagungsbandOA Gold
InformatikFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Titel in Englisch:
On the Use of Feature-based Collaborative Explanations : An Empirical Comparison of Explanation Styles
Autor*in:
Naveed, Sidra
;
Loepp, BenediktUDE
GND
1232038113
LSF ID
54109
ORCID
0000-0001-9059-5324ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
;
Ziegler, JürgenUDE
GND
1015876811
LSF ID
3881
ORCID
0000-0001-9603-5272ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
Open Access?:
OA Gold
Scopus ID
Sprache des Textes:
Englisch
Schlagwort, Thema:
collaborative filtering ; explanations ; user experience

Abstract in Englisch:

Current attempts to explain recommendations mostly exploit a single type of data, i.e. usually either ratings provided by users for items in collaborative filtering systems, or item features in content-based systems. While this might be sufficient in straightforward recommendation scenarios, the complexity of other situations could require the use of multiple datasources, for instance, depending on the product domain. Even though hybrid systems have a long and successful history in recommender research, the connections between user ratings and item features have only rarely been used for offering more informative and transparent explanations. In previous work, we presented a prototype system based on a feature-weighting mechanism that constitutes an exception, allowing to recommend both items and features based on ratings while offering advanced explanations based on content data. In this paper, we empirically evaluate this prototype in terms of user-oriented aspects and user experience against to widely accepted baselines. Two user studies show that our novel approach outperforms conventional collaborative filtering, while a pure content-based system was perceived in a similarly positive light. Overall, the results draw a promising picture, which becomes particularly apparent from a user perspective when participants were specifically asked to use the explanations: they indicated in their qualitative feedback that they understood them and highly appreciated their availability.