Abstract
Context aware recommender systems (CARS) adapt to the specific situation in which the recommended item will be consumed. So, for instance, music recommendations while the user is traveling by car should take into account the current traffic condition or the driver’s mood. This requires the acquisition of ratings for items in several alternative contextual situations, to extract from this data the true dependency of the ratings on the contextual situation. In this paper, in order to simplify the in-context rating acquisition process, we consider the individual perceptions of the users about the influence of context on their decisions. We have elaborated a system design methodology where we assume that users can be requested to judge: a) if a contextual factor (e.g., the traffic state) is relevant for their decision making task, and b) how they would rate an item assuming that a certain contextual condition (e.g., traffic is chaotic) holds. Using these evaluations we show that it is possible to build an effective context-aware mobile recommender system.
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References
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 217–256. Springer, Heidelberg (2011)
Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing (to appear, 2011)
Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: RecSys 2010: Proceedings of the 2010 ACM Conference on Recommender Systems, pp. 119–126 (2010)
Cai, R., Zhang, C., Wang, C., Zhang, L., Ma, W.Y.: Musicsense: contextual music recommendation using emotional allocation modeling. In: MULTIMEDIA 2007: Proceedings of the 15th International Conference on Multimedia, pp. 553–556. ACM, New York (2007)
Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 596–627. Springer, Heidelberg (2007)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2008: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM, New York (2008)
Koren, Y.: Collaborative filtering with temporal dynamics. In: KDD 2009: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456. ACM, New York (2009)
Lee, J.S., Lee, J.C.: Context awareness by case-based reasoning in a music recommendation system. In: Ichikawa, H., Cho, W.-D., Chen, Y., Youn, H.Y. (eds.) UCS 2007. LNCS, vol. 4836, pp. 45–58. Springer, Heidelberg (2007)
Li, C.T., Shan, M.K.: Emotion-based impressionism slideshow with automatic music accompaniment. In: MULTIMEDIA 2007: Proceedings of the 15th International Conference on Multimedia, pp. 839–842. ACM Press, New York (2007)
Reddy, S., Mascia, J.: Lifetrak: music in tune with your life. In: HCM 2006: Proceedings of the 1st ACM International Workshop on Human-Centered Multimedia, New York, NY, USA, pp. 25–34 (2006)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Heidelberg (2011)
Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5), 293–302 (2002)
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Baltrunas, L. et al. (2011). InCarMusic: Context-Aware Music Recommendations in a Car. In: Huemer, C., Setzer, T. (eds) E-Commerce and Web Technologies. EC-Web 2011. Lecture Notes in Business Information Processing, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23014-1_8
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DOI: https://doi.org/10.1007/978-3-642-23014-1_8
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