Hernandez-Bocanegra, Diana Carolina; Ziegler, Jürgen:
ConvEx-DS : A dataset for conversational explanations in recommender systems
In: Interfaces and Human Decision Making for Recommender Systems 2021 : Proceedings of the 8th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems - 8th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS 2021), 25-29 September 2021, online - Aachen: RWTH Aachen, 2021 - (CEUR Workshop Proceedings ; 2948), S. 3 - 20
2021Buchaufsatz/Kapitel in TagungsbandOA Platin
InformatikFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Damit verbunden: 1 Publikation(en)
Titel in Englisch:
ConvEx-DS : A dataset for conversational explanations in recommender systems
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
Open Access?:
OA Platin
Scopus ID
Sprache des Textes:
Englisch
Schlagwort, Thema:
Conversational agent ; Dataset ; Explanations ; Recommender systems ; User study

Abstract in Englisch:

Conversational explanations are a novel and promising means to support users’ understanding of the items proposed by a recommender system (RS). Providing details about items and the reasons why they are recommended in a conversational, language-based style allows users to question recommendations in a flexible, user-controlled manner, which may increase the perceived transparency of the system. However, little is known about the impact and implications of providing such explanations, using for example a conversational agent (CA). In particular, there is a lack of datasets that facilitate the implementation of dialog systems with explanatory purposes in RS. In this paper we validate the suitability of an intent model for explanations in the domain of hotels, collecting and annotating 1806 questions asked by study participants, and addressing the perceived helpfulness of the responses generated by an explainable RS using such intent model. Thus, we release an English dataset (ConvEx-DS), containing intent annotations of users’ questions, which can be used to train intent classifiers, and to implement a dialog system with explanatory purpose in the domain of hotels.