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Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space

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Title: Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space
Authors: Hirakawa, Taisei Browse this author
Maeda, Keisuke Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Asamizu, Satoshi Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Correlation
Business process re-engineering
Information science
Image edge detection
Electronic commerce
Edge pruning
cross-domain recommendation
latent space graph convolutional networks
Issue Date: 11-Jan-2022
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: IEEE Access
Volume: 10
Start Page: 12503
End Page: 12509
Publisher DOI: 10.1109/ACCESS.2022.3142187
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.
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: article
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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