Neural Variational Collaborative Filtering for Top-K Recommendation

Abstract

Collaborative Filtering (CF) is one of the most widely applied models for recommender systems. However, CF-based methods suffer from data sparsity and cold-start, more attention has been drawn to hybrid methods by using both the rating and content information. Variational Autoencoder (VAE) has been confirmed to be highly effective in CF task, due to its Bayesian nature and non-linearity. Nevertheless, most VAE models suffer from data sparsity, which leads to poor latent representations of users and items. Besides, most existing VAE-based methods model either user latent factors or item latent factors, which makes them unable to recommend items to a new user or recommend a new item to existing users. To address these problems, we propose a novel deep hybrid framework for top-K recommendation, named Neural Variational Collaborative Filtering (NVCF), where user and item side information is incorporated into the generative processes of user and item, to alleviate data sparsity and learn better latent representations of users and items. For inference purpose, we derived a Stochastic Gradient Variational Bayes (SGVB) algorithm to approximate the intractable distributions of latent factors of users and items. Experiments performed on two public datasets have showed our method significantly outperforms the state-of-the-art CF-based and VAE-based methods.