Abstract
In this paper, we present refining graph representation for cross-domain recommendation (CDR) based on edge pruning considering feature distribution in a latent space. Conventional graph-based CDR methods have utilized all ratings and purchase histories of user’s products. However, some items purchased by users are not related to the domain for recommendation, and this information becomes noise when making CDR. So, the proposed method introduces edge pruning into the latest graph-based CDR method to refine graph representation. To compare the item embedding features calculated in different domains, we construct a latent space and perform edge pruning through their correlations. Additionally, we introduce a state-of-the-art graph neural network into the graph construction of the proposed method that considers the interactions between users and items thereby obtaining effective embedding features in a domain. This makes it possible to consider domain-specific user preferences and estimate embedding features with high-expressive power. Furthermore, to compare the embedding features of items in the two domains, we construct their latent spaces and project them. Edge pruning is performed using the correlation of items between the two domains on the latent space. We obtain cross domain specific graph representation through edge pruning, which improves the performance by considering the relationship between both items across domains. To the best of our knowledge, no study in the CDR field focuses on eliminating unnecessary node information. We have demonstrated the effectiveness of the proposed method by comparing several graph-based state-of-the-art methods.
Highlights
The number of e-commerce services has increased rapidly providing a variety of services
We propose a refining graph representation for cross-domain recommendation (CDR) based on edge pruning considering feature distribution in a latent space
We introduce edge pruning into the new graph-based CDR method for refining graph representation
Summary
The number of e-commerce services has increased rapidly providing a variety of services. Previous graph-based methods use the information of all the items for which interactions exist in the source domain, some of those items have nothing to do with the target domain. It means that there is a possibility that the users’ embedding features were calculated using the information of those items that contain noise. It is expected that pruning the edges of items in the source domain that are irrelevant for item recommendation in the target domain improves the performance of the CDR. We propose a refining graph representation for CDR based on edge pruning considering feature distribution in a latent space. Note that this paper is an extension of [24]
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