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Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space
| 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 | | Licenses | | History | | 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 |
| URI: | http://hdl.handle.net/2115/84316 |
| 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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