AutoFM: A hybrid collaborative filtering model with denoising autoencoders and factorization machine

Abstract

Collaborative filtering (CF) has achieved great performance in recommender system over past decades. CF-based methods firstly map users and items to latent factors which share the same latent space, and then use a linear function to predict user ratings on items, such as inner product or cosine distance. It only uses original latent feature, however feature interactions are usually helpful in enhancing recommendation performance. To tackle such issue, we used Factorization Machines (FM) to enhanced linear methods by incorporating the second-order feature interactions. In this paper, we propose a novel hybrid model, AutoFM, which combine Denoising Autoencoder (DAE) and FM together. AutoFM follows collaborative filtering method, it firstly uses DAE to map users and items to latent factor, then it uses FM calculating user ratings on items. To tackle the cold start problem, we also take as the input of FM user’s and item’s side information besides of latent factor. We conduct AutoFM on three real-world datasets, and the experiment results show that AutoFM consistently outperforms the state-of-the-art method.