Cataldo Musto
Marco de Gemmis
Giovanni Semeraro
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 multi-criteria 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.
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Index Terms
- A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users' Reviews
Recommendations
Justifying Recommendations through Aspect-based Sentiment Analysis of Users Reviews
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A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews
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