CDR-ULPT:Cross-domain recommendation for e-commerce using user-level preferences transfer network

Abstract

Recommender systems have been widely used on e-commerce websites because they can assist users in finding products that fit their needs among the masses of products. However, recommender systems face the problem of data sparsity. Cross-domain recommender systems can alleviate this question and have been a hot research area. Adversarial domain adaptation has been a hot topic in cross-domain recommender systems due to its ability to mitigate the differences in feature distribution among different domains. Recent research has focused on learning users' domain sharing and private preferences. However, adversarial domain adaptation models ignore the individualized personal distinctions of users' domain-sharing preferences in the alignment process. In addition, these models missed indirect interaction. Graph convolutional model can mine the indirect interaction but ignore the different influences from neighboring users. Therefore, this paper proposes a Cross-domain Recommendation for e-commerce using the User-level Preferences Transfer Network (CDR-ULPT). The main work is as follows: (1) Adding an attention mechanism on adversarial domain adaptation to personalize the alignment of users' domain-sharing preferences. (2) Capturing users' indirect interactions using multiple graph convolution layers, adding node attention on the graph convolution layer to dynamically aggregate neighbor features. Experiments on five public datasets demonstrate that the method proposed in this work outperforms other cross-domain recommendation methods.