Cluster searching strategies for collaborative recommendation systems

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Altingovde İ. S., Subakan O. N., Ulusoy O.

INFORMATION PROCESSING & MANAGEMENT, vol.49, no.3, pp.688-697, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 49 Issue: 3
  • Publication Date: 2013
  • Doi Number: 10.1016/j.ipm.2012.07.008
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.688-697
  • Keywords: Collaborative filtering, Clustering, Inverted index, CONTEXT
  • Middle East Technical University Affiliated: Yes


In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster-skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures. (C) 2012 Elsevier Ltd. All rights reserved.