DITN: User’s indirect side-information involved domain-invariant feature transfer network for cross-domain recommendation

Abstract

The cross-domain recommendation aims to make more accurate personalized recommendations by transferring user preferences from a relatively rich interactive information domain to a sparse data domain. Traditional methods mostly only use direct side-information to improve the generalization of user and item representations which is usually limited or introduce indirect side-information without fully considering the problems of noise reduction and balanced fusion. For these two considerations, we propose a User’s Indirect Side-information Involved Domain-invariant Feature Transfer Network for Cross-Domain Recommendation (DITN) to mutually suppress and balanced fuse direct and indirect side-information. To effectively suppress the noise caused by adding side-information, we define a group of global property representations to guide the user’s feature extraction of direct and indirect side-information and adopt the co-attention mechanism to balanced fuse two kinds of side-information features, which improves the quality of the user’s preferences learning. Then we propose a novel user’s feature disentangle module based on the VAE framework to extract the invariant and the specific features between domains and enhance feature disentangling by utilizing mutual information to reduce the negative transfer problem. We have conducted sufficient experiments on 6 domains and 3 scenarios which include 2,408,007 interactive information from the Amazon dataset to demonstrate the significant performance of our proposed model. The results show that DITN effectively enhances the rating prediction accuracy and outperforms state-of-the-art recommendation methods.