Assessing the Helpfulness of Review Content for Explaining Recommendations

TL;DR

This paper explores how review content can be used to generate effective explanations for recommendations, emphasizing the importance of credibility and convincingness in perceived helpfulness.

Contribution

It introduces an argument-based method for automatically extracting helpful review content to improve transparency in recommender systems.

Findings

Perception of credibility influences helpfulness.

Convincingness mediates helpfulness perception.

Proposed argument-based extraction approach.

Abstract

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.