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User satisfaction in long term group recommendations

dc.book.titleCase-Based Reasoning Research and Development, ICCBR 2011
dc.contributor.authorQuijano Sánchez, Lara
dc.contributor.authorRecio García, Juan Antonio
dc.contributor.authorDíaz Agudo, María Belén
dc.date.accessioned2023-06-20T05:44:53Z
dc.date.available2023-06-20T05:44:53Z
dc.date.issued2011
dc.description© 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 (Group 910494).
dc.description.abstractIn 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.
dc.description.departmentSección Deptal. de Arquitectura de Computadores y Automática (Físicas)
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.sponsorshipSpanish Ministry of Science & Education
dc.description.sponsorshipMadrid Education Council
dc.description.sponsorshipUCM
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/20391
dc.identifier.isbn978-3-642-23290-9
dc.identifier.officialurlhttp://link.springer.com/content/pdf/10.1007%2F978-3-642-23291-6_17
dc.identifier.relatedurlhttp://link.springer.com/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/45403
dc.language.isoeng
dc.page.final225
dc.page.initial211
dc.publisherSpringer-Verlag Berlín
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence
dc.relation.projectIDTIN2009-13692-C03-03
dc.relation.projectIDGroup 910494
dc.rights.accessRightsopen access
dc.subject.cdu004.8
dc.subject.keywordSystems
dc.subject.ucmInformática (Informática)
dc.subject.unesco1203.17 Informática
dc.titleUser satisfaction in long term group recommendations
dc.typebook part
dc.volume.number6880
dcterms.references1. 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 225 8. 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)
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relation.isAuthorOfPublication.latestForDiscovery95de81bf-4637-4307-8ff6-f2c06c591d18

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