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. © 2012 Springer-Verlag.
CITATION STYLE
Leginus, M., Dolog, P., & Žemaitis, V. (2012). Improving tensor based recommenders with clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7379 LNCS, pp. 151–163). https://doi.org/10.1007/978-3-642-31454-4_13
Mendeley helps you to discover research relevant for your work.