Neural variational collaborative filtering with side information for top-K recommendation

Abstract

Collaborative filtering (CF) is one of the most widely applied models for recommender systems. Despite its success, CF-based methods suffer from rating sparsity and cold-start problem, which leads to poor quality of recommendations. Previous studies have gave great attention to construct hybrid methods, by incorporating side information and user rating. Variational autoencoder (VAE) has been confirmed to be highly effective in CF task, due to its Bayesian nature and non-linearity. However, rating sparsity remains a great challenge to most VAE models, which leads to poor latent user/item representations. In addition, most existing VAE-based methods model either latent user factors or latent item factors, resulting in the incapacity to recommend items to a new user or suggest a new item to existing users. To address these problems, we design a novel deep hybrid framework for top-k recommendation, neural variational collaborative filtering (NVCF), and propose three NVCF-based instantiation. In generative process, the side information of user and item is incorporated to alleviate rating sparsity, for learning better latent user/item representations. In inference process, a Stochastic Gradient Variational Bayes approach is employed to approximate the unmanageable distributions of latent user/item factors. Experiments performed on four public datasets have indicated our methods significantly outperform the state-of-the-art hybrid CF models and VAE-based methods.