Assessing the Helpfulness of Review Content for Explaining Recommendations

Hernandez-Bocanegra, D. C., & Ziegler, J. (2019). EARS Workshop at SIGIR’19.


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 ar- gumentative 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 percep- tion 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 argument- based approach to automatically extracting helpful content from hotel reviews, a domain that differs from those that best fit classical argumentation theories.


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