A VAE-Based User Preference Learning and Transfer Framework for Cross-Domain Recommendation

Abstract

The core idea of cross-domain recommendation is to alleviate the problem of data scarcity. Previous methods have made brilliant successes. However, many of them mainly focus on learning an ideal mapping function across-domains, ignoring the user preferences within a specific domain, which leads to suboptimal results. In this paper, we propose a Cross-Domain Recommendation Variational AutoEncoder framework (CDRVAE), a novel extension of a variational autoencoder on cross-domain recommendations for user behaviour distribution modeling. It applies a new hybrid architecture of VAE as the backbone and simultaneously constructs two information flows, within-domain and cross-domain modeling. For the former, an asymmetric codec structure is designed to reconstruct preference distribution from domain-specific latent factors. To relieve the posterior collapse dilemma, a combined prior is employed to increase the distribution complexity. The equivalent transition by a transformation matrix and the unobserved interaction generation by cross-domain reconstruction contribute to the latter. We combine all the above components for the more accurate and reliable user features. Extensive experiments are conducted on three public benchmark datasets to validate the effectiveness of the proposed CDRVAE. Experimental results demonstrate that CDRVAE is consistently superior to other state-of-the-art alternative baseline models.