Cross-Domain Variational Autoencoder for Recommender Systems

Abstract

Aiming at the problem of sparse data and user cold start in a single domain for collaborative filtering, this paper proposes a cross-domain recommendation model called cross-domain variational autoencoder (CDVAE) based on Bayesian theory. The model uses rating data to construct latent representations of users and items in multiple domains, utilizes overlapping users in different domains, and draws on the idea of variational autoencoder (VAE) to train a user generating networks cross-domains based on user preferences to different domains. Bayesian theory is introduced to ensure the generation ability and robustness of the model. Finally, based on the user’s preference to the source domain, the user’s preference prediction in the target domain is obtained. The generation of the recommendation result is obtained by the user’s preference weighting in the source domain and the target domain, and the user’s multi-source interest preference is considered to improve the accuracy. The experimental results show that CDVAE is able to significantly outperform the state-of-the-art recommendation methods of more robust performance.