Local Latent Space Models For Top-N Recommendation

Authors:
Evangelia Christakopoulou University of Minnesota
George Karypis University of Minnesota

Introduction:

This paper studies Users’ behaviors . The authors consider models in which there are some latent factors that capture the shared aspects and some user subset specific latent factors that capture the set of aspects that the different subsets of users care about.

Abstract:

Users’ behaviors are driven by their preferences across various aspects of items they are potentially interested in purchasing, viewing, etc. Latent space approaches model these aspects in the form of latent factors. Although such approaches have been shown to lead to good results, the aspects that are important to different users can vary. In many domains, there may be a set of aspects for which all users care about and a set of aspects that are specific to different subsets of users. To explicitly capture this, we consider models in which there are some latent factors that capture the shared aspects and some user subset specific latent factors that capture the set of aspects that the different subsets of users care about.

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