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Socially-Aware Recommendation for Over-Constrained Problems

  • Muesluem AtasEmail author
  • Thi Ngoc Trang Tran
  • Alexander Felfernig
  • Ralph Samer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)

Abstract

Group recommender systems support the identification of items that best fit the individual preferences of all group members. A group recommendation can be determined on the basis of aggregation functions. However, to some extent it is still unclear which aggregation function is most suitable for predicting an item to a group. In this paper, we analyze different preference aggregation functions with regard to their prediction quality. We found out that consensus-based aggregation functions (e.g., Average, Minimal Group Distance, Multiplicative, Ensemble Voting) which consider all group members’ preferences lead to a better prediction quality compared to borderline aggregation functions, such as Least Misery and Most Pleasure which solely focus on preferences of some individual group members.

Keywords

Group recommender systems Decision making Group aggregation functions Similarity metrics 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Muesluem Atas
    • 1
    Email author
  • Thi Ngoc Trang Tran
    • 1
  • Alexander Felfernig
    • 1
  • Ralph Samer
    • 1
  1. 1.Institute of Software TechnologyGraz University of TechnologyGrazAustria

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