Traditionally, recommender systems exploit user ratings to infer preferences. However, the growing popularity of social platforms has encouraged users to write textual reviews about liked items. These reviews represent a valuable source of non-trivial information that could improve users' decision processes. In this paper we propose a novel recommendation approach based on the semantic annotation of entities mentioned in user reviews and on the knowledge available in the Web of Data. We compared our recommender system with two baseline algorithms and a state-of-the-art Linked Data based approach. Our system provided more diverse recommendations with respect to the other techniques considered, while obtaining a better accuracy than the Linked Data based method.

SemRevRec: a recommender system based on user reviews and linked data / Vagliano, Iacopo; Monti, DIEGO MICHELE; Morisio, Maurizio. - ELETTRONICO. - Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (Poster-RecSys 2017):(2017). ((Intervento presentato al convegno 11th ACM Conference on Recommender Systems tenutosi a Como (Italy) nel August 28, 2017.

SemRevRec: a recommender system based on user reviews and linked data

MONTI, DIEGO MICHELE;MORISIO, MAURIZIO
2017

Abstract

Traditionally, recommender systems exploit user ratings to infer preferences. However, the growing popularity of social platforms has encouraged users to write textual reviews about liked items. These reviews represent a valuable source of non-trivial information that could improve users' decision processes. In this paper we propose a novel recommendation approach based on the semantic annotation of entities mentioned in user reviews and on the knowledge available in the Web of Data. We compared our recommender system with two baseline algorithms and a state-of-the-art Linked Data based approach. Our system provided more diverse recommendations with respect to the other techniques considered, while obtaining a better accuracy than the Linked Data based method.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2678773
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