Hernandez-Bocanegra, Diana Carolina; Ziegler, Jürgen:
Assessing the Helpfulness of Review Content for Explaining Recommendations
In: EARS 2019 : The 2nd International Workshop on ExplainAble Recommendation and Search - 2nd International Workshop on ExplainAble Recommendation and Search, EARS’19, 25 July 2019, Paris, France - New York: ACM, 2019
2019book article/chapter in ProceedingsOA Green
Computer ScienceFaculty of Engineering » Computer Science and Applied Cognitive Science » Computer Science
Title in English:
Assessing the Helpfulness of Review Content for Explaining Recommendations
Author:
Hernandez-Bocanegra, Diana CarolinaORCID iDLSF; Ziegler, JürgenLSF
Open Access?:
OA Green
arXiv.org ID:
Language of text:
English
Keyword, Topic:
Recommender systems, explanations

Abstract in English:

Despite the maturity already achieved by recommender systems algorithms, little is known about how to obtain and provide users with a proper rationale for a recommendation. Transparency and effectiveness of recommender systems may be increased when explanations are provided. In particular, identifying of helpful argumentative content from reviews can be leveraged to generate textual explanations. In this paper, we investigate the reasons why a review might be considered helpful, and show that the perception of credibility and convincingness mediates the relationship between helpfulness and the perception of objectivity and relevant aspects addressed. Our findings led us to suggest an argumentbased approach to automatically extracting helpful content from hotel reviews, a domain that differs from those that best fit classical argumentation theories.