Improving Tensor Based Recommenders with Clustering

Martin Leginus, Peter Dolog, Valdas Zemaitis

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

11 Citations (Scopus)


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
Original languageEnglish
Title of host publicationUser Modeling, Adaptation, and Personalization : 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings
Number of pages10
Place of PublicationBerlin
Publication date2012
ISBN (Print)978-3-642-31453-7
ISBN (Electronic)978-3-642-31454-4
Publication statusPublished - 2012
EventUser Modeling, Adaptation, and Personalization - Montreal, Canada
Duration: 16 Jul 201220 Jul 2012
Conference number: 20


ConferenceUser Modeling, Adaptation, and Personalization
SeriesLecture Notes in Computer Science


  • tensor factorization
  • clustering.

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  • Projects

    MEco: MEco - Medical Ecosystem - Personalized Event-Based Surveillance

    Dolog, P., Xu, G., Lage, R. G., Bayyapu, K. R. & Pan, R.

    FP7 IST


    Project: Research

  • Cite this

    Leginus, M., Dolog, P., & Zemaitis, V. (2012). Improving Tensor Based Recommenders with Clustering. In User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings (Vol. 7379, pp. 151-163). Springer. Lecture Notes in Computer Science, Vol.. 7379