Interest Level Estimation via Multi-Modal Gaussian Process Latent Variable Factorization

Abstract

This paper presents a method of interest level estimation via multimodal Gaussian process latent variable factorization (mGPLVF). The proposed method estimates user interest levels for contents with high accuracy by using multi-modal features such as contents and users' behavior. Generally, users' behavior includes some noise, and it is difficult to prepare a large amount of data. For dealing with the problem, the proposed method newly derives mGPLVF calculating appropriate latent variables that do not overfit a small amount of training data including noise based on a probabilistic generative model. Furthermore, mGPLVF simultaneously performs not only construction of the robust latent space but also estimation of user interest levels via the latent variables based on an idea inspired by a factorization machine. The consistent framework of latent space construction and interest level estimation leads to the improvement of the final estimation accuracy. Experimental results show the effectiveness of the proposed method.