Global and Local Tensor Factorization for Multi criteria Recommender System
Summary In multi criteria recommender systems, matrix factorization characterizes users and items via latent factor vectors inferred from user item rating patterns. However, two dimensional matrix factorization models may not be able to cope with the recommendation problem that involves additional criterion specific rating data. This study introduces a tensor factorization method to handle three dimensional user item criterion rating data. Moreover, we observe that using single global tensor factorization alone may not be sufficient to characterize diverse preferences among different groups of users, and a combined global and local tensor factorization method (GLTF) for multi criteria recommendation is thus proposed. One key benefit of the GLTF is that it can leverage global user item criterion rating patterns while also exploiting local user subset specific rating behaviors to jointly infer the latent factor representations for users, items, and specific item criteria. Experimental results, which used real life data available to the public, demonstrated that the GLTF is superior to well established baseline methods.
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