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Recommendation to Groups

  • Anthony Jameson
  • Barry Smyth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4321)

Abstract

Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that arise, organizing them in terms of four subtasks that can or must be dealt with by a group recommender: 1. acquiring information about the user’s preferences; 2. generating recommendations; 3. explaining recommendations; and 4. helping users to settle on a final decision. For each issue, we discuss how it has been dealt with in existing group recommender systems and what open questions call for further research.

Keywords

Recommender System Group Recommender Individual Group Member Explicit Preference Animated Character 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Anthony Jameson
    • 1
  • Barry Smyth
    • 2
  1. 1.DFKI, German Research Center for Artificial Intelligence 
  2. 2.Department of Computer Science, University College Dublin 

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