Abstract
The growth of available entertainment information services, such as movies and CD listings, or travels and recreational activities, raises a need for personalization techniques for filtering and adapting contents to customer’s interest and needs. Personalization technologies rely on users data, represented as User Models (UMs). UMs built by specific services are usually not transferable due to commercial competition and models’ representation heterogeneity. This paper focuses on the second obstacle and discusses architecture for mediating UMs across different domains of entertainment. The mediation facilitates improving the accuracy of the UMs and upgrading the provided personalization.
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References
Aguzzoli, S., Avesani, P., Massa, P.: Collaborative Case-Based Recommender System. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, p. 460. Springer, Heidelberg (2002)
Berkovsky, S.: Ubiquitous User Modeling in Recommender Systems. In: Proceedings of the UM Conference, Edinbirgh, UK (2005)
Berkovsky, S., Busetta, P., Eytani, Y., Kuflik, T., Ricci, F.: Collaborative Filtering over Distributed Environment. In: Proceedings of the Workshop on Decentralized, Agent-Based and Social Approaches to User Modeling, Edinburgh, UK (2005)
Cranor, L.F., Reagle, J., Ackerman, M.S.: Beyond Concern: Understanding Net Users’ Attitudes about Online Privacy, Technical report, AT&T Labs-Research (1999)
Dai, W., Cohen, R.: Dynamic Personalized TV Recommendation System. In: Proceedings of the Workshop on Personalization in Future TV, Pittsburgh, PA (2003)
Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining Collaborative Filtering with Personal Agents for Better Recommendations. In: Proceedings of the AAAI Conference, Orlando, FL (1999)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval Journal 4(2), 133–151 (2001)
Hanani, U., Shapira, B., Shoval, P.: Information Filtering: Overview of Issues, Research and Systems. User Modeling and User Adapted Interactions 11(3), 203–259 (2001)
Kay, J., Kummerfeld, B., Lauder, P.: Managing Private User Models and Shared Personas. In: Proceedings of the Workshop on UM for Ubiquitous Computing, Pittsburgh, PA (2003)
Kobsa, A.: Generic User Modeling Systems. User Modeling and User-Adapted Interaction 11(1-2), 49–63 (2001)
Niederee, C., Stewart, A., Mehta, B., Hemmje, M.: A Multi-Dimensional, Unified User Model for Cross-System Personalization. In: Proceedings of the Workshop on Environments for Personalized Information Access, Gallipoli, Italy (2004)
Resnick, P., Varian, H.R.: Recommender Systems. Communications of the ACM 40(3), 56–58 (1997)
Ricci, F., Arslan, B., Mirzadeh, N., Venturini, A.: ITR: a Case-Based Travel Advisory System. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, p. 613. Springer, Heidelberg (2002)
Smyth, B., Cotter, P.: The Sky’s the Limit: A Personalised TV Listings Service for the Digital TV Age. In: Proceedings of the ES Conference, Cambridge, UK (1999)
Stock, O., Zancanaro, M., Not, E.: Intelligent Interactive Information Presentation for Cultural Tourism. In: Stock, O., Zancanaro, M. (eds.) Multimodal Intelligent Information Presentation. Springer, Heidelberg (2005)
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Berkovsky, S., Kuflik, T., Ricci, F. (2005). Entertainment Personalization Mechanism Through Cross-Domain User Modeling. In: Maybury, M., Stock, O., Wahlster, W. (eds) Intelligent Technologies for Interactive Entertainment. INTETAIN 2005. Lecture Notes in Computer Science(), vol 3814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590323_22
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DOI: https://doi.org/10.1007/11590323_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30509-5
Online ISBN: 978-3-540-31651-0
eBook Packages: Computer ScienceComputer Science (R0)