In this paper we propose a multi-criteria recommender system based on collaborative filtering (CF) techniques, which exploits the information conveyed by users' reviews to provide a multi-faceted representation of users' interests. To this end, we exploited a framework for opinion mining and sentiment analysis, which automatically extracts relevant aspects and sentiment scores from users' reviews. As an example, in a restaurant recommendation scenario, the aspects may regard food quality, service, position, athmosphere of the place and so on. Such a multi-faceted representation of the user is used to feed a multicriteria CF algorithm which predicts user interest in a particular item and provides her with recommendations. In the experimental session we evaluated the performance of the algorithm against several state-of-the-art baselines; Results confirmed the insight behind this work, since our approach was able to overcome both single-criteria recommendation algorithms as well as more sophisticated techniques based on matrix factorization.

A multi-criteria recommender system exploiting aspect-based sentiment analysis of users' reviews

Musto, Cataldo;De Gemmis, Marco;Semeraro, Giovanni;Lops, Pasquale
2017-01-01

Abstract

In this paper we propose a multi-criteria recommender system based on collaborative filtering (CF) techniques, which exploits the information conveyed by users' reviews to provide a multi-faceted representation of users' interests. To this end, we exploited a framework for opinion mining and sentiment analysis, which automatically extracts relevant aspects and sentiment scores from users' reviews. As an example, in a restaurant recommendation scenario, the aspects may regard food quality, service, position, athmosphere of the place and so on. Such a multi-faceted representation of the user is used to feed a multicriteria CF algorithm which predicts user interest in a particular item and provides her with recommendations. In the experimental session we evaluated the performance of the algorithm against several state-of-the-art baselines; Results confirmed the insight behind this work, since our approach was able to overcome both single-criteria recommendation algorithms as well as more sophisticated techniques based on matrix factorization.
2017
9781450346528
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/225411
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