Abstract
Context-aware recommender systems aim at outperforming traditional context-free recommenders by exploiting information about the context under which the users’ ratings are acquired. In this paper we present a novel contextual pre-filtering approach that takes advantage of the semantic similarities between contextual situations. For assessing context similarity we rely only on the available users’ ratings and we deem as similar two contextual situations that are influencing in a similar way the user’s rating behavior. We present an extensive comparative evaluation of the proposed approach using several contextually-tagged ratings data sets. We show that it outperforms state-of-the-art context-aware recommendation techniques.
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References
Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems 23(1), 103–145 (2005)
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.-B. (eds.) Recommender Systems Handbook, pp. 217–250. Springer (2011)
Ahn, J., Brusilovsky, P., Grady, J., He, D., Syn, S.: Open user profiles for adaptive news systems: help or harm? In: Proceedings of the 16th International Conference on World Wide Web, pp. 11–20 (2007)
Baltrunas, L., Amatriain, X.: Towards time-dependant recommendation based on implicit feedback. In: Workshop on Context-Aware Recommender Systems, CARS 2009 (2009)
Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., Lüke, K.-H., Schwaiger, R.: InCarMusic: Context-Aware Music Recommendations in a Car. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 89–100. Springer, Heidelberg (2011)
Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing 16(5), 507–526 (2012)
Baltrunas, L., Ludwig, B., Ricci, F.: Matrix factorization techniques for context aware recommendation. In: Proceedings of the 2011 ACM Conference on Recommender Systems, Chicago, pp. 301–304 (2011)
Baltrunas, L., Ricci, F.: Context-dependent items generation in collaborative filtering. In: Proceedings of the 2009 ACM Conference on Recommender Systems, New York, pp. 245–249 (2009)
Cantador, I., Castells, P., Bellogín, A.: An Enhanced Semantic Layer for Hybrid Recommender Systems: Application to News Recommendation. International Journal on Semantic Web and Information Systems 7(1), 44–77 (2011)
Codina, V., Ceccaroni, L.: Semantically-Enhanced Recommenders. In: Proceedings of the 15th International Conference of the Catalan Association for Artificial Intelligence, pp. 69–78. IOS Press (2012)
Codina, V., Ricci, F., Ceccaroni, L.: Semantically-Enhanced Pre-filtering for Context-Aware Recommender Systems. In: 3rd ACM Workshop on Context-Awareness in Retrieval and Recommendation, CaRR 2013, Rome, Italy, pp. 15–18 (2013)
Dumais, S.: LSA and information retrieval: Getting back to basics. In: Landauer, T.-K., McNamara, D.-S., Dennis, S., Kintsch, W. (eds.) LSA: A Road to Meaning, pp. 293–321. Lawrence Earlbaum (2006)
Karatzoglou, A., Amatriain, X., Baltrunas, L., Olivier, N.: Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering. In: Proceedings of the 2010 ACM Conference on Recommender Systems, Barcelona, Spain, pp. 79–86 (2010)
Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM 40(3), 77–87 (1997)
Koren, Y., Bell, R.: Advances in Collaborative Filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.-B. (eds.) Recommender Systems Handbook, pp. 145–186. Springer (2011)
Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
Mobasher, B., Jin, X., Zhou, Y.: Semantically Enhanced Collaborative Filtering on the Web. In: Berendt, B., Hotho, A., Mladenič, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds.) EWMF 2003. LNCS (LNAI), vol. 3209, pp. 57–76. Springer, Heidelberg (2004)
Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., Pedone, A.: Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In: Proceedings of the 2009 ACM Conference on Recommender Systems, New York, pp. 265–268 (2009)
Shani, G., Gunawardana, A.: Evaluating Recommendation Systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.-B. (eds.) Recommender Systems Handbook, pp. 257–297. Springer (2011)
Sieg, A., Mobasher, B., Burke, R.: Ontological User Profiles for Personalized Web Search. In: Proceedings of the AAAI 2007 Workshop on Intelligent Techniques for Web Personalization, pp. 84–91 (2007)
Zheng, Y., Burke, R., Mobasher, B.: Optimal feature selection for context-aware recommendation using differential relaxation. In: 4th ACM Workshop on Context-Aware Recommender Systems, CARS 2012 (2012)
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Codina, V., Ricci, F., Ceccaroni, L. (2013). Exploiting the Semantic Similarity of Contextual Situations for Pre-filtering Recommendation. 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_14
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DOI: https://doi.org/10.1007/978-3-642-38844-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38843-9
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