Speeding up tensor based recommenders with clustered tag space and improving quality of recommendations with Non-negative tensor factorization

Leginus, Martin ; Žemaitis, Valdas

2. term

Computer Science, Master

2011

2011-05-31

30 pages

Social tagging facilitates users to annotate and categorize items (Web links, pictures, movies, etc.). Assigned tags express the user preferences. Social tagging systems (STS) have become popular hence various tag based recommenders have been developed. The state-of-the-art 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 (CD), etc. STS accumulate large amount of data that signicantly slows down the process of a tensor factorization. Firstly, we propose to reduce tag space by exploiting clustering techniques so that execution time is improved and memory requirements are decreased while preserving the quality of the recommendations. The clustering is motivated by the fact that many tags in a tag space are semantically similar thus the tags can be grouped. Secondly, we propose to incorporate the personal prior knowledge to increase the precision of tensor based recommenders. In addition, we take an advantage of Non-negative Tensor Factorization (NTF) to get rid of negative values from the factorized tensor that are dicult to interpret. Finally, we combine all the approaches to improve the quality and time of computations and present the promising experimental results.

personal tensor factorization ; clustering ; tags ; higher order singular value decomposition ; non-negative tensor factorization ; prior knowledge ; parallel

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