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), pp. 3 - 20
2021book article/chapter in ProceedingsOA Platinum
Computer ScienceFaculty of Engineering » Computer Science and Applied Cognitive Science » Computer Science » Interactive Systems
Title in English:
ConvEx-DS : A dataset for conversational explanations in recommender systems
Author:
Hernandez-Bocanegra, Diana CarolinaSCOPUSLSF; Ziegler, JürgenSCOPUSLSF
Open Access?:
OA Platinum
Scopus ID:
Language of text:
English
Keyword, Topic:
Conversational agent ; Dataset ; Explanations ; Recommender systems ; User study

Abstract in English:

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.