Abstract
Today most of existing personalization systems (e.g. content recommenders, or targeted ad) focus on individual users and ignore the social situation in which the services are consumed. However, many human activities are social and involve several individuals whose tastes and expectations must be taken into account by the service providers. When a group profile is not available, different profile aggregation strategies can be applied to recommend adequate content and services to a group of users based on their individual profiles. In this paper, we consider an approach intended to determine the factors that influence the choice of an aggregation strategy. We present a preliminary evaluation made on a real large-scale dataset of TV viewings, showing how group interests can be predicted by combining individual user profiles through an appropriate strategy. The conducted experiments compare the group profiles obtained by aggregating individual user profiles according to various strategies to the “reference” group profile obtained by directly analyzing group consumptions.
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References
Aghasaryan, A., Betgé-Brezetz, S., Senot, C., Toms, Y.: A Profiling Engine for Converged Service Delivery Platforms. Bell Labs Tech. J. 13(2), 93–103 (2008)
Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: INTRIGUE: Personalized Recommendation of Tourist Attractions for Desktop and Handset Devices. Applied Artificial Intelligence: Special Issue on Artificial Intelligence for Cultural Heritage and Digital, Libraries 17(8-9), 687–714 (2003)
BARB: Broadcaster Audience Research Board, http://www.barb.co.uk
Bernier, C., Brun, A., Aghasaryan, A., Bouzid, M., Picault, J., Senot, C., Boyer, A.: Topology of communities for the collaborative recommendations to groups. In: 3rd International Conference on Information Systems and Economic Intelligence (SIIE), Tunisia (2010)
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, Gallipoli, Italy, pp. 48–54. ACM Press, New York (2004)
McCarthy, J.F., Anagnost, T.D.: MusicFX: An Arbiter of Group Preferences for Computer Supported Collaborative Workouts. In: ACM 1998 Conference on CSCW (1998)
McCarthy, J.F., Costa, T.J., Liongosari, E.S.: UniCast, OutCast & GroupCast: Three Steps Toward Ubiquitous, Peripheral Displays. In: Abowd, G.D., Brumitt, B., Shafer, S. (eds.) UbiComp 2001. LNCS, vol. 2201, pp. 332–345. Springer, Heidelberg (2001)
McCarthy, K., Salamó, M., McGinty, L., Smyth, B.: CATS: A synchronous approach to collaborative group recommendation. In: Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, Melbourne Beach, FL, pp. 86–91. AAAI Press, Menlo Park (2006)
Masthoff, J.: Modeling a group of television viewers. In: Proceedings of the Future TV: Adaptive instruction in your living room workshop, associated with ITS02 (2002)
Masthoff, J.: Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers. User Modeling and User-Adapted Interaction 14, 37–85 (2004)
Masthoff, J., Gatt, A.: In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Modeling and User-Adapted Interaction 16(3-4), 281–319 (2006)
MovieLens, http://www.movielens.org
O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: PolyLens: A Recommender System for Groups of Users. In: Proceedings of ECSCW, pp. 199–218 (2001)
Yu, Z., Zhou, X., Hao, Y., Gu, J.: A TV program recommendation for multiple viewers based on user profile merging. User Modeling and User-Adapted Interaction 16(1), 63–82 (2006)
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Senot, C., Kostadinov, D., Bouzid, M., Picault, J., Aghasaryan, A., Bernier, C. (2010). Analysis of Strategies for Building Group Profiles. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_6
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DOI: https://doi.org/10.1007/978-3-642-13470-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13469-2
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