Juan A. Recio-Garcia
ABSTRACT
In this article we introduce a novel method of making recommendations to groups based on existing techniques of collaborative filtering and taking into account the group personality composition. We have tested our method in the movie recommendation domain and we have experimentally evaluated its behavior under heterogeneous groups according to the group personality composition.
- Y.-L. Chen, L.-C. Cheng, and C.-N. Chuang. A group recommendation system with consideration of interactions among group members. Expert Syst. Appl., 34(3):2082--2090, 2008. Google Scholar
Digital Library
- A. Jameson and B. Smyth. Recommendation to groups. The Adaptive Web, LNCS 4321:596--627, 2007. Google ScholarDigital Library
- J. Kelleher and D. Bridge. An accurate and scalable collaborative recommender. Artificial Intelligence Review, 21(3-4):193--213, 2004. Google ScholarDigital Library
- J. Masthoý and A. Gatt. 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. Google ScholarDigital Library
- M. O'Connor, D. Cosley, J. A. Konstan, and J. Riedl. Polylens: a recommender system for groups of users. In Procs. of ECSCW'01, pages 199--218, Norwell, MA, USA, 2001. Kluwer Academic Publishers. Google ScholarDigital Library
- J. A. Recio-Garcia, B. Diaz--Agudo, and P. A. Gonzalez-Calero. Prototyping Recommender Systems in jCOLIBRI. In Procs. of RecSys '08, ACM, pages 243--250, 2008. Google ScholarDigital Library
- M. Tamiz, D. Jones, and C. Romero. Goal programming for decision making: An overview of the current state-of-the-art. European Journal of Operational Research, 111:569--581, 1998.Google ScholarCross Ref
- K. Thomas and R. Kilmann. Thomas-Kilmann Conflict Mode Instrument. Tuxedo, N.Y., 1974.Google Scholar
Index Terms
- Personality aware recommendations to groups
Recommendations
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