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Improving Tensor Based Recommenders with Clustering

  • Martin Leginus
  • Peter Dolog
  • Valdas Žemaitis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7379)

Abstract

Social tagging systems (STS) model three types of entities (i.e. tag-user-item) and relationships between them are encoded into a 3-order tensor. Latent relationships and patterns can be discovered by applying tensor factorization techniques like Higher Order Singular Value Decomposition (HOSVD), Canonical Decomposition etc. STS accumulate large amount of sparse data that restricts factorization techniques to detect latent relations and also significantly slows down the process of a factorization. We propose to reduce tag space by exploiting clustering techniques so that the quality of the recommendations and execution time are improved and memory requirements are decreased. The clustering is motivated by the fact that many tags in a tag space are semantically similar thus the tags can be grouped. Finally, promising experimental results are presented.

Keywords

tensor factorization HOSVD clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Martin Leginus
    • 1
  • Peter Dolog
    • 1
  • Valdas Žemaitis
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityDenmark

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