In this paper we present a methodology to justify the suggestions generated by a recommendation algorithm through the identification of relevant and distinguishing characteristics of the recommended item, automatically extracted by mining users' reviews. Our approach relies on a combination of natural language processing and sentiment analysis techniques, and is based on the following steps: (1) a set of users' reviews discussing the recommended item is gathered and analyzed; (2) the distinguishing aspects that characterize the item are extracted and a ranking function is used to identify the most relevant ones; (3) excerpts of the reviews discussing such aspects are extracted and a natural language template is filled in through the aggregation of these sentences. This represents the final output of the algorithm, which is provided to the user as justification of the recommendation she received. In the experimental evaluation, we carried out a user study (N=296, 73.6% male) aiming to investigate the effectiveness of our methodology in two different domains, as movies and books. Results showed that our technique can provide users with rich and satisfying justifications. Moreover, our experiment also showed that the users prefer review-based justifications to other explanation strategies, and this finding further confirmed the effectiveness of the approach.

Justifying recommendations through aspect-based sentiment analysis of users' reviews

Musto C.;Lops P.;De Gemmis M.;Semeraro G.
2019

Abstract

In this paper we present a methodology to justify the suggestions generated by a recommendation algorithm through the identification of relevant and distinguishing characteristics of the recommended item, automatically extracted by mining users' reviews. Our approach relies on a combination of natural language processing and sentiment analysis techniques, and is based on the following steps: (1) a set of users' reviews discussing the recommended item is gathered and analyzed; (2) the distinguishing aspects that characterize the item are extracted and a ranking function is used to identify the most relevant ones; (3) excerpts of the reviews discussing such aspects are extracted and a natural language template is filled in through the aggregation of these sentences. This represents the final output of the algorithm, which is provided to the user as justification of the recommendation she received. In the experimental evaluation, we carried out a user study (N=296, 73.6% male) aiming to investigate the effectiveness of our methodology in two different domains, as movies and books. Results showed that our technique can provide users with rich and satisfying justifications. Moreover, our experiment also showed that the users prefer review-based justifications to other explanation strategies, and this finding further confirmed the effectiveness of the approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/231959
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