Variational Autoencoder-Based Hybrid Recommendation With Poisson Factorization for Modeling Implicit Feedback

Abstract

Hybrid recommendation, which is based on collaborative filtering and supplemented with auxiliary content information, is being actively researched due to its ability to overcome the cold-start problem. Many proposed hybrid methods make recommendations using Gaussian distribution-based collaborative filtering even though they handle variables that tend to be non-Gaussian, such as the number of interactions. We present a method that uses a hybrid recommendation framework based on collaborative filtering that models the number of interactions as a Poisson-distributed and variational autoencoder-based content information generation process that shares latent variables with collaborative filtering. As a prior for the shared latent variables, we use a gamma distribution, which is a conjugate prior of a Poisson distribution. An implicit-derivative-based reparameterization trick enables the use of a gamma distribution in a variational autoencoder. The latent variables in the generative model are inferred using the stochastic gradient variational Bayes approach, taking the number of interactions corresponding to users and items and content information as input. In accordance with the inference, unobserved interactions between users and items are predicted for recommendation. The use of a neural-network-based generative model for content information enables the framework to handle various types of content information. Experimental results show that the proposed method utilizes content information effectively for predicting the number of interactions and that it should aid in overcoming the cold-start problem.