Abstract
Since today’s television can receive more and more programs, and televisions are often viewed by groups of people, such as a family or a student dormitory, this paper proposes a TV program recommendation strategy for multiple viewers based on user profile merging. This paper first introduces three alternative strategies to achieve program recommendation for multiple television viewers, discusses, and analyzes their advantages and disadvantages respectively, and then chooses the strategy based on user profile merging as our solution. The selected strategy first merges all user profiles to construct a common user profile, and then uses a recommendation approach to generate a common program recommendation list for the group according to the merged user profile. This paper then describes in detail the user profile merging scheme, the key technology of the strategy, which is based on total distance minimization. The evaluation results proved that the merging result can appropriately reflect the preferences of the majority of members within the group, and the proposed recommendation strategy is effective for multiple viewers watching TV together.
Similar content being viewed by others
References
Ardissono L., Goy A., Petrone G., Segnan M., Torasso P. (2003). INTRIGUE: personalized recommendation of tourist attractions for desktop and handset devices. Appl. Artif. Int. 17(8–9):687–714
Bollen, J.: Group user models for personalized hyperlink recommendations. In: Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, LNCS 1892, pp. 38–50 (2000)
Bozios, T., Lekakos, G., Skoularidou, V., Chorianopoulos, K.: Advanced techniques for personalized advertising in a digital TV environment: the iMEDIA system. In: Proceedings of the eBusiness and eWork Conference, pp. 107–113. Venice, Italy (2000)
Dalal, M.: 1988, Updates in Propositional Databases. Technical Report DCS-TR-222, Department of Computer Science, Rutgers University
Das, D., ter Horst, H.: Recommender Systems for TV. In: Recommender Systems, Papers from the 1998 Workshop, Technical Report WS-98–08, Madison, WI, Menlo, Park, pp. 35–36. AAAI Press, CA (1998)
Ehrmantraut, M., Härder, T., Wittig, H., Steinmetz, R.: The personal electronic program guide – towards the pre-selection of individual TV programs. In: Proceedings of the 5th International Conference on Information and Knowledge Management (CIKM’96), pp. 243–250. Rockville, MD, USA (1996)
Gutta, S., Kurapati, K., Lee, K. P., Martino, J., Milanski, J., Schaffer, J. D., Zimmerman, J.: TV content recommender system. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, pp. 1121–1122. Austin, TX, USA, (2000)
Hull, D.: Using statistical testing in the evaluation of retrieval experiments. In: Proceedings of the 16th International ACM SIGIR Conference, pp. 329–338. New York, USA (1993)
Jameson, A.: More than the sum of its members: challenges for group recommender systems. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, pp. 48–54. Gallipoli, Italy (2004)
Lieberman, H., Dyke, N.W.V., Vivacqua A.S.: Let’s Browse: a collaborative web browsing agent. In: Proceedings of the International Conference on Intelligent User Interfaces (IUI99), pp. 65–68. ACM Press, New York (1999)
Masthoff J. (2004). Group modeling: selecting a sequence of television items to suit a group of viewers User Model. User-Adapt. Interact. J. Personalization Res. 14(1):37–85
McCarthy, J.F., Anagnost, T.D.: MusicFX: an arbiter of group preferences for computer supported collaborative workouts. In: Proceedings of the 1998 Conference on Computer-Supported Cooperative Work, pp. 363–372. Seattle, WA, USA (1998)
O’Connor, M., Cosley, D., Konstan, J., Riedl, J.: PolyLens: a recommender system for groups of users. In: Proceedings of the European Conference on Computer-Supported Cooperative Work, pp.~199–218. Bonn, Germany (2001)
O’Sullivan D., Smyth B., Wilson D.C., McDonald K., Smeaton A. (2004) Improving the quality of the personalized electronic program guide User Model and User-Adapt. Interact. J. Personalization Res. 14(1):5–36
Rijsbergen, C.J.: Information Retrieval. Butterworths, 2nd edn. London, UK (1979)
Salton G. (1989). Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley Longman Publishing, Boston, MA, USA
Smyth B.P., Cotter P. (2004). Case-studies on the evolution of the personalized electronic program guide. In: Ardissono L., Kobsa A., Maybury M.T. (eds) Personalized Digital Television: Targeting Programs to Individual Viewers. Kluwer Academic Publishers, Dordrecht Netherlands
Yu Z.W., Zhou X.S. (2004). TV3P: an adaptive assistant for personalized TV. IEEE Transactions Consum. Electron. 50(1):393–399
Yu, Z.W., Zhou, X.S., Hao, Y.B., Gu, J.H.: User profile merging based on total distance minimization. In: Proceedings of the 2nd International Conference On Smart homes and health Telematics (ICOST 2004), pp. 25–32. IOS Press, Singapore (2004)
Yu, Z.W., Zhou, X.S., Zhang D.Q.: An adaptive in-vehicle multimedia recommender for group users. In: Proceedings of IEEE 61st Vehicular Technology Conference (VTC 2005-Spring), pp.~2800–2804. Stockholm, Sweden (2005)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yu, Z., Zhou, X., Hao, Y. et al. TV Program Recommendation for Multiple Viewers Based on user Profile Merging. User Model User-Adap Inter 16, 63–82 (2006). https://doi.org/10.1007/s11257-006-9005-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11257-006-9005-6