Improving Group Recommendations by Identifying Homogenous Subgroups

  • Maytiyanin Komkhao
  • Jie Lu
  • Zhong Li
  • Wolfgang A. Halang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 214)

Abstract

Recommender systems have proven their effectiveness in supporting personalised purchasing decisions and e-service intelligence. In order to support members in user groups of recommender systems, recently designed group recommender systems search for data relevant to all group members and discover the agreements between members of online communities. This paper focuses on achieving common satisfaction for groups or communities by, e.g. finding a restaurant for a family or shoes for a group of cheerleaders. It establishes an algorithm, called I-GRS, to devise group recommender systems based on incremental model-based collaborative filtering and applying the Mahalanobis distance and fuzzy membership to create groups of users with similar interests. Finally, an algorithm and related design strategy to build group recommender systems is proposed. A set of experiments is set up to evaluate the performance of the I-GRS algorithm in group recommendations. The results show its effectiveness vis-à-vis the recommendations made by classical recommender systems to single or groups of individuals.

Keywords

Group recommender systems Subgroups Rating confidence Incremental group recommender systems Model-based collaborative filtering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Maytiyanin Komkhao
    • 1
  • Jie Lu
    • 2
  • Zhong Li
    • 1
  • Wolfgang A. Halang
    • 1
  1. 1.Chair of Computer EngineeringFernuniversität in HagenHagenGermany
  2. 2.Decision Systems and e-Service Intelligence Laboratory, School of Software, Faculty of Engineering and ITUniversity of Technology SydneyBroadwayAustralia

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