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Despite the maturity already achieved by recommender systems algorithms,little is known about how to obtain and provide users with a proper rationalefor a recommendation. Transparency and effectiveness of recommender systems maybe increased when explanations are provided. In particular, identifying ofhelpful argumentative content from reviews can be leveraged to generate textualexplanations. In this paper, we investigate the reasons why a review might beconsidered helpful, and show that the perception of credibility andconvincingness mediates the relationship between helpfulness and the perceptionof objectivity and relevant aspects addressed. Our findings led us to suggestan argumentbased approach to automatically extracting helpful content fromhotel reviews, a domain that differs from those that best fit classicalargumentation theories.

Assessing the Helpfulness of Review Content for Explaining Recommendations