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| Content Provider | ACM Digital Library |
|---|---|
| Author | de Gemmis, Marco Semeraro, Giovanni Lops, Pasquale Musto, Cataldo |
| 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. |
| Starting Page | 321 |
| Ending Page | 325 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450346528 |
| DOI | 10.1145/3109859.3109905 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-08-27 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Sentiment analysis Opinion mining Recommender systems |
| Content Type | Text |
| Resource Type | Article |
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