RT Book, Section
T1 User satisfaction in long term group recommendations
A1 Quijano Sánchez, Lara
A1 Recio García, Juan A.
A1 Díaz Agudo, Belén
AB In this paper we introduce our application HappyMovie, a Facebook application for movie recommendation to groups. This system takes advantage of social data available in this social network to promote fairness for the provided recommendations. Group recommendations are based in the individual satisfaction of each individual. The(in)satisfaction of users modifies the typical aggregation functions used to estimate the value of an item for the group. This paper proposes a memory of past recommendations to compute the satisfaction of users when similar items (movies, in this case) are recommended several times.
PB Springer-Verlag Berlín
SN 978-3-642-23290-9
YR 2011
FD 2011
LK https://hdl.handle.net/20.500.14352/45403
UL https://hdl.handle.net/20.500.14352/45403
LA eng
NO 1. Recio-García, J.A., Jiménez-Díaz, G., Sánchez-Ruiz, A.A., Díaz-Agudo, B.: Personality aware recommendations to groups. In: Bergman, L.D., Tuzhilin, A., Burke, R.D., Felfernig, A., Schmidt-Thieme, L. (eds.) Procs. of the 2009 ACM Conference on Recommender Systems, pp. 325–328. ACM, New York (2009)2. Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B.: Social based recommendations to groups. In: Procs. of the 14th UK Workshop on Case-Based Reasoning, pp. 46–57. CMS Press, University of Greenwich (2009)3. Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B.: Personality and social trust in group recommendations. In: Procs. of the 22th International Conference on Tools with Artificial Intelligence, ICTAI 2010 (to appear, 2010)4. Quijano-Sánchez, L., Recio-García, J.A., Díaz-Agudo, B., Jiménez-Díaz, G.: Social factors in group recommender systems. In: ACM-TIST, TIST-2011-01-0013 (to be published, 2011)5. 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, 281–319 (2006)6. Barsade, S.G.: The ripple effect: Emotional contagion and its influence on group behavior. Administrative Science Quarterly 47, 644–675 (2002)7. Hatfield, E., Cacioppo, J., Rapson, R.: Emotional Contagion. Studies in Emotion and Social Interaction. Cambridge University Press, Cambridge (1994). User Satisfaction in Long Term Group Recommendations 2258. McCarthy, J.F., Anagnost, T.D.: MusicFX: An arbiter of group preferences for computer aupported collaborative workouts. In: CSCW 1998: Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work, pp. 363–372. ACM, New York (1998)9. Crossen, A., Budzik, J., Hammond, K.J.: Flytrap: intelligent group music recommendation. In: IUI, pp. 184–185 (2002)10. Baccigalupo, C., Plaza, E.: A case-based song scheduler for group customised radio. In: Weber, R., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 433–448. Springer, Heidelberg (2007)11. Aamodt, A., Plaza, E.: Case-based reasoning:  foundational issues,methodological variants, and system approaches. Artificial Intelligence Communications 7, 39–59 (1994)12. Thomas, K., Kilmann, R.: Thomas-Kilmann Conflict Mode Instrument, Tuxedo, N.Y. (1974)13. Díaz-Agudo, B., González-Calero, P.A., Recio-García, J.A., Sánchez-Ruiz-Granados, A.A.: Building cbr systems with jcolibri. Sci. Comput. Program. 69, 68–75 (2007)14. Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)15. O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: Polylens: a recommender system for groups of users. In: ECSCW 2001: Proceedings of the Seventh Conference on European Conference on Computer Supported Cooperative Work, pp. 199–218. Kluwer Academic Publishers, Norwell (2001)16. 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)17. Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowl.-Based Syst. 22, 261–265 (2009)18. Kelleher, J., Bridge, D.G.: An accurate and scalable collaborative recommender. Artif. Intell. Rev. 21, 193–213 (2004)
NO © Springer Verlag Berlin Heidelberg 2011. International Conference on Case-Based Reasoning (19th. Sep 11-14, 2011. London, England). Supported by Spanish Ministry of Science & Education(TIN2009-13692-C03-03) and Madrid Education Council and UCM (Group910494).
NO Spanish Ministry of Science & Education
NO Madrid Education Council
NO UCM
DS Docta Complutense
RD 30 abr 2024