Abstract
Rating prediction is a well-known recommendation task aiming to predict a user’s rating for those items which were not rated yet by her. Predictions are computed from users’ explicit feedback, i.e. their ratings provided on some items in the past. Another type of feedback are user reviews provided on items which implicitly express users’ opinions on items. Recent studies indicate that opinions inferred from users’ reviews on items are strong predictors of user’s implicit feedback or even ratings and thus, should be utilized in computation. As far as we know, all the recent works on recommendation techniques utilizing opinions inferred from users’ reviews are either focused on the item recommendation task or use only the opinion information, completely leaving users’ ratings out of consideration. The approach proposed in this paper is filling this gap, providing a simple, personalized and scalable rating prediction framework utilizing both ratings provided by users and opinions inferred from their reviews. Experimental results provided on a dataset containing user ratings and reviews from the real-world Amazon Product Review Data show the effectiveness of the proposed framework.
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References
Aciar, S., Zhang, D., Simoff, S., Debenham, J.: Informed Recommender: Basing Recommendations on Consumer Product Reviews. IEEE Intelligent Systems 22, 3 (2007)
Adamovicius, G., Tuzhilin, A.: Context-Aware Recommender Systems. In: Ricci, F., et al. (eds.) Recommender Systems Handbook. Springer (2011) ISBN 978-0-387-85819-7
Faridani, S., Bitton, E., Ryokai, K., Goldberg, K.: Opinion space: a scalable tool for browsing online comments. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems. ACM, New York (2010)
Faridani, S.: Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search. In: Proceedings of the 5th ACM Conference on Recommender Systems. ACM, New York (2011)
Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: MyMediaLite: A Free Recommender System Library. In: Proceedings of the 5th ACM International Conference on Recommender Systems, Chicago, USA (2011)
Ganu, G., Elhadad, N., Marian, A.: Beyond the Stars: Improving Rating Predictions using Review Text Content. In: 12th International Workshop on the Web and Databases, Providence, Rhode Island, USA (2009)
Gemulla, R., Nijkamp, E., Haas, P.J., Sismanis, Y.: Large-scale matrix factorization with distributed stochastic gradient descent. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York (2011)
Hu, M., Liu, B.: Mining and Summarizing Customer Reviews. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Seattle, Washington, USA (2004)
Jindal, N., Liu, B.: Opinion Spam and Analysis. In: Proceedings of First ACM International Conference on Web Search and Data Mining (WSDM-2008), Stanford University, Stanford, California, USA (2008)
Ko, M., Kim, H.W., Yi, M.Y., Song, J., Liu, Y.: Movie Commenter: Aspect-based collaborative filtering by utilizing user comments. In: 7th International Conference on Collaborative Computing, Orlando, FL, USA (2011)
Koren, Y., Bell, R., Volinsky, C.: Matrix Factorization Techniques for Recommender Systems. Computer 42(8) (2009)
Kuroiwa, T., Bhalla, S.: Aizu-BUS: need-based book recommendation using web reviews and web services. In: Bhalla, S. (ed.) DNIS 2007. LNCS, vol. 4777, pp. 297–308. Springer, Heidelberg (2007)
Leung, C.W.K., Chan, S.C.F., Chung, F.: Integrating collaborative filtering and sentiment analysis: A rating inference approach. In: Proceedings of the ECAI 2006 Workshop on Recommender Systems (2006)
Lippert, C., Weber, S.H., Huang, Y., Tresp, V., Schubert, M., Kriegel, H.-P.: Relation Prediction in Multi-Relational Domains using Matrix Factorization. In: NIPS Workshop on Structured Input Structure Output (2008)
Liu, B.: Sentiment Analysis and Subjectivity. In: Handbook of Natural Language Processing, 2nd edn. (2010)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2) (2008)
Pilászy, I., Tikk, D.: Recommending new movies: even a few ratings are more valuable than metadata. In: Proceedings of the Third ACM Conference on Recommender Systems. ACM, New York (2009)
Poirier, D., Fessant, F., Tellier, I.: Reducing the Cold-Start Problem in Content Recommendation through Opinion Classification. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE Computer Society, Washington, DC (2010)
Raghavan, S., Gunasekar, S., Ghosh, J.: Review quality aware collaborative filtering. In: Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys 2012). ACM, New York (2012)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer (2011)
Singh, A.P., Gordon, G.J.: A Unified View of Matrix Factorization Models. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 358–373. Springer, Heidelberg (2008)
Singh, V.K., Mukherjee, M., Mehta, G.K.: Combining collaborative filtering and sentiment classification for improved movie recommendations. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds.) MIWAI 2011. LNCS, vol. 7080, pp. 38–50. Springer, Heidelberg (2011)
Wu, M.: Collaborative filtering via ensembles of matrix factorizations. In: KDD Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA (2007)
Zhang, W., Ding, G., Chen, L., Li, C.: Augmenting Chinese Online Video Recommendations by Using Virtual Ratings Predicted by Review Sentiment Classification. In: Proceedings of the 2010 IEEE ICDM Workshops. IEEE Computer Society, Washington, DC (2010)
Zhang, W., Ding, G., Chen, L., Li, C.: Augmenting Online Video Recommendations by Fusing Review Sentiment Classification. In: Proceedings of the 2nd ACM RecSys 2010 Workshop on Recommender Systems and the Social Web. ACM (2010)
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Pero, Š., Horváth, T. (2013). Opinion-Driven Matrix Factorization for Rating Prediction. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_1
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DOI: https://doi.org/10.1007/978-3-642-38844-6_1
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