Abstract
Nowadays, recommender systems are widely used in various domains to help customers access to more satisfying products or services. It is expected that exploiting customers’ contextual information can improve the quality of recommendation results. Most earlier researchers assume that they already have customers’ explicit ratings on items and each rating has customer’s abstracted context (e.g. summer, morning). However, in practical applications, it is not easy to obtain customers’ explicit ratings and their abstract-level contexts. We aim to acquire customers’ preferences and their context by exploiting the information implied in the customers’ previous event logs and to adopt them into a well known recommendation technique, Collaborative Filtering (CF). In this paper, we show how to obtain customers’ implicit preferences from event logs and present a strategy to abstract context information from event logs considering fuzziness in context. In addition, we present several methods to cooperate achieved contextual information and preferences into CF. To evaluate and compare our methods, we conducted several empirical experiments using a set of music listening logs obtained from last.fm, and the results indicate that our methods can improve the quality of recommendation.
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References
Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23(1), 103–145 (2005)
Baltrunas, L.: Exploiting contextual information in recommender systems. In: RecSys 2008: Proceedings of the 2008 ACM conference on Recommender systems, pp. 295–298. ACM, New York (2008)
Baltrunas, L., Amatriain, X.: Towards Time-Dependant Recommendation based on Implicit Feedback. In: Proceedings of Workshop on Context-Aware Recommender Systems (CARS-2009) in conjunction with the 3rd ACM Conference on Recommender Systems (2009)
Bojadziev, G., Bojadziev, M.: Fuzzy Sets, Fuzzy Logic, Applications, pp. 113–140. World Scientific Publishing Co. Pte. Ltd., Singapore (1995)
Chen, A.: Context-aware collaborative filtering system: predicting the user’s preferences in ubiquitous computing. In: CHI 2005: CHI 2005 extended abstracts on Human factors in computing systems, pp. 1110–1111. ACM, New York (2005)
Dey, A.K.: Understanding and using context. Personal Ubiquitous Comput. 5(1), 4–7 (2001)
Gemmell, J., Bell, G., Lueder, R.: MyLifeBits: a personal database for everything. Communications of the ACM 49(1), 95 (2006)
Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22(1), 53 (2004)
Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: CHI 1995: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 194–201. ACM Press/Addison-Wesley Publishing Co. (1995)
Lee, S., Gong, G., Lee, S.G.: Lifelogon: Log on to your lifelog ontology! In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, Springer, Heidelberg (2009)
Melucci, M., White, R.: Utilizing a geometry of context for enhanced implicit feedback. In: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp. 273–282. ACM, New York (2007)
Park, H.S., Yoo, J.O., Cho, S.B.: A context-aware music recommendation system using fuzzy bayesian networks with utility theory. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, ch. 121, pp. 970–979. Springer, Heidelberg (2006)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW 1994: Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186. ACM, New York (1994)
van Setten, M., Pokraev, S., Koolwaaij, J.: Context-aware recommendations in the mobile tourist application compass. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 235–244. Springer, Heidelberg (2004)
Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: CHI 1995: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co. (1995)
Shin, D., Lee, J.W., Yeon, J., Lee, S.G.: Context-aware recommendation by aggregating user context. In: CEC 2009: Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing, pp. 423–430. IEEE Computer Society, Los Alamitos (2009)
Weng, S.S., Lin, B., Chen, W.T.: Using contextual information and multidimensional approach for recommendation. Expert Syst. Appl. 36(2), 1268–1279 (2009)
Whorf, B.L.: Language, thought and reality: Selected writings of Benjamin Lee Whorf. MIT Press, Cambridge (1956)
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Lee, D., Park, S.E., Kahng, M., Lee, S., Lee, Sg. (2010). Exploiting Contextual Information from Event Logs for Personalized Recommendation. In: Lee, R. (eds) Computer and Information Science 2010. Studies in Computational Intelligence, vol 317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15405-8_11
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DOI: https://doi.org/10.1007/978-3-642-15405-8_11
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