ABSTRACT
Recommender systems are widely used to minimize the information overload problem. A great source of information is users' reviews, since they provide both item descriptions and users' opinions. Recent works that process reviews often neglect problems such as polysemy and sinonimy. On the other hand, systems that rely on word sense disambiguation focus their efforts on items's static descriptions. In this paper, we propose a hybrid recommender system that uses word sense disambiguation and entity linking to produce concept-based item representations extracted from users' reviews. Our findings suggest that adding such semantics to items' representations have a positive impact on recommendations.
- J. Bobadilla, F. Ortega, A. Hernando, and A. GutiéRrez. 2013. Recommender Systems Survey. Know.-Based Syst. 46 (July 2013), 109--132. https://doi.org/10. 1016/j.knosys.2013.03.012Google Scholar
- Michel Capelle, Flavius Frasincar, Marnix Moerland, and Frederik Hogenboom. 2012. Semantics-based News Recommendation. In Proceedings of the 2Nd International Conference on Web Intelligence, Mining and Semantics (WIMS '12). ACM, New York, NY, USA, Article 27, 9 pages. https://doi.org/10.1145/2254129.2254163Google ScholarDigital Library
- Li Chen, Guanliang Chen, and Feng Wang. 2015. Recommender systems based on user reviews: the state of the art. User Modeling and User-Adapted Interaction 25, 2 (01 Jun 2015), 99--154. https://doi.org/10.1007/s11257-015--9155--5 Google ScholarDigital Library
- Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. 2016. Learning to Rank Features for Recommendation over Multiple Categories. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '16). ACM, New York, NY, USA, 305--314. https://doi.org/10.1145/ 2911451.2911549Google ScholarDigital Library
- Rafael D'Addio, Merley Conrado, Solange Resende, and Marcelo Manzato. 2014. Generating Recommendations Based on Robust Term Extraction from Users' Reviews. In Proceedings of the 20th Brazilian Symposium on Multimedia and the Web (WebMedia '14). ACM, New York, NY, USA, 55--58. https://doi.org/10.1145/ 2664551.2664583Google ScholarDigital Library
- Rafael Martins D'Addio and Marcelo Garcia Manzato. 2014. A collaborative filtering approach based on user's reviews. In Intelligent Systems (BRACIS), 2014 Brazilian Conference on. IEEE, 204--209. https://doi.org/10.1109/BRACIS.2014.45 Google ScholarDigital Library
- Rafael M. D'Addio and Marcelo G. Manzato. 2016. Exploiting Item Representations for Soft Clustering Recommendation. In Proceedings of the 22Nd Brazilian Symposium on Multimedia and the Web (Webmedia '16). ACM, New York, NY, USA, 271--278. https://doi.org/10.1145/2976796.2976858 Google ScholarDigital Library
- Gayatree Ganu, Yogesh Kakodkar, and AméLie Marian. 2013. Improving the Quality of Predictions Using Textual Information in Online User Reviews. Information Systems 38, 1 (March 2013), 1--15. https://doi.org/10.1016/j.is.2012.03.001Google ScholarDigital Library
- Yehuda Koren. 2010. Factor in the Neighbors: Scalable and Accurate Collaborative Filtering. ACM Trans. Knowl. Discov. Data 4, 1, Article 1 (Jan. 2010), 24 pages. https://doi.org/10.1145/1644873.1644874Google ScholarDigital Library
- Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Marco De Gemmis, Pierpaolo Basile, and Giovanni Semeraro. 2010. MARS: A MultilAnguage Recommender System. In Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec '10). ACM, New York, NY, USA, 24--31. https://doi.org/10.1145/1869446.1869450 Google ScholarDigital Library
- Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA. Google ScholarCross Ref
- Julian McAuley, Rahul Pandey, and Jure Leskovec. 2015. Inferring Networks of Substitutable and Complementary Products. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15). ACM, New York, NY, USA, 785--794. https://doi.org/10.1145/2783258.2783381 Google ScholarDigital Library
- Andrea Moro, Alessandro Raganato, and Roberto Navigli. 2014. Entity linking meets word sense disambiguation: a unified approach. Transactions of the Association for Computational Linguistics 2 (2014), 231--244.Google ScholarCross Ref
- Cataldo Musto, Giovanni Semeraro, Pasquale Lops, and Marco de Gemmis. 2014. Combining Distributional Semantics and Entity Linking for Context-Aware Content- Based Recommendation. Springer International Publishing, Cham, 381--392. https: //doi.org/10.1007/978--3--319-08786--3_34 Google ScholarCross Ref
- Fedelucio Narducci, Pierpaolo Basile, Cataldo Musto, Pasquale Lops, Annalina Caputo, Marco de Gemmis, Leo Iaquinta, and Giovanni Semeraro. 2016. Conceptbased Item Representations for a Cross-lingual Content-based Recommendation Process. Inf. Sci. 374, C (Dec. 2016), 15--31. https://doi.org/10.1016/j.ins.2016.09. 022Google Scholar
- Roberto Navigli and Simone Paolo Ponzetto. 2012. BabelNet: The Automatic Construction, Evaluation and Application of a Wide-coverage Multilingual Semantic Network. Artif. Intell. 193 (Dec. 2012), 217--250. https://doi.org/10.1016/j. artint.2012.07.001Google Scholar
- Sergio Oramas, Vito Claudio Ostuni, Tommaso Di Noia, Xavier Serra, and Eugenio Di Sciascio. 2016. Sound and Music Recommendation with Knowledge Graphs. ACM Trans. Intell. Syst. Technol. 8, 2, Article 21 (Oct. 2016), 21 pages. https://doi.org/10.1145/2926718Google ScholarDigital Library
- Maria Terzi, Matthew Rowe, Maria-Angela Ferrario, and Jon Whittle. 2014. Text- Based User-kNN: Measuring User Similarity Based on Text Reviews. Springer International Publishing, Cham, 195--206. https://doi.org/10.1007/978--3--319-08786--3_ 17Google Scholar
Index Terms
- Semantic Organization of User's Reviews Applied in Recommender Systems
Recommendations
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