Hernandez-Bocanegra, Diana Carolina; Ziegler, Jürgen:
Conversational review-based explanations for recommender systems : Exploring users' query behavior
In: CUI 2021 - 3rd Conference on Conversational User Interfaces - CUI '21; Virtual, Online; 27 July 2021 - 29 July 2021 - New York: Association for Computing Machinery (ACM), 2021 - (ACM International Conference Proceeding Series), Artikel 1
2021Buchaufsatz/Kapitel in Tagungsband
InformatikFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Damit verbunden: 1 Publikation(en)
Titel in Englisch:
Conversational review-based explanations for recommender systems : Exploring users' query behavior
Autor*in:
Hernandez-Bocanegra, Diana CarolinaUDE
GND
1256307963
LSF ID
60092
ORCID
0000-0002-1773-2633ORCID iD
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
Scopus ID
Sprache des Textes:
Englisch
Schlagwort, Thema:
argumentation ; conversational agent ; explanations ; Recommender systems ; user study

Abstract in Englisch:

Providing explanations based on user reviews in recommender systems (RS) can increase users' perception of system transparency. While static explanations are dominant, interactive explanatory approaches have emerged in explainable artificial intelligence (XAI), so that users are more likely to examine system decisions and get more arguments supporting system assertions. However, little attention has been paid to conversational approaches for explanations targeting end users. In this paper we explore how to design a conversational interface to provide explanations in a review-based RS, and present the results of a Wizard of Oz (WoOz) study that provided insights into the type of questions users might ask in such a context, as well as their perception of a system simulating such a dialog. Consequently, we propose a dialog management policy and user intents for explainable review-based RS, taking as an example the hotels domain.