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User Satisfaction in Long Term Group Recommendations

  • Lara Quijano-Sánchez
  • Juan A. Recio-García
  • Belén Díaz-Agudo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6880)

Abstract

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.

Keywords

User Satisfaction Aggregation Function Case Base Reasoning Global Satisfaction Computer Support Cooperative Work 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lara Quijano-Sánchez
    • 1
  • Juan A. Recio-García
    • 1
  • Belén Díaz-Agudo
    • 1
  1. 1.Dep. Ingeniería del Software e Inteligencia ArtificialUniversidad Complutense de MadridSpain

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