Cross-domain recommendation based on multilayer graph analysis using subgraph representation

Taisei Hirakawa; Keisuke Maeda; Takahiro Ogawa; Satoshi Asamizu; Miki Haseyama

doi:10.1117/12.2590988

13 March 2021 Cross-domain recommendation based on multilayer graph analysis using subgraph representation

Taisei Hirakawa, Keisuke Maeda, Takahiro Ogawa, Satoshi Asamizu, Miki Haseyama

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 1176635 (2021) https://doi.org/10.1117/12.2590988
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

This paper presents cross-domain recommendation based on multilayer graph analysis using subgraph representation. The proposed method constructs two graphs in source and target domains utilizing user-item embedding and trains link relationships between the users’ embedding features on each above graph via graph convolutional networks considering subgraph representation. Thus, the proposed method can obtain features with high representation ability, and this is the main contribution of this paper. Then the proposed method can estimate the user’s embedding features in the target domain from those in the source domain and recommend items to users by using the estimated features. Experiments on real-world e-commerce datasets verify the effectiveness of the proposed method.

Citation Download Citation

Taisei Hirakawa, Keisuke Maeda, Takahiro Ogawa, Satoshi Asamizu, and Miki Haseyama "Cross-domain recommendation based on multilayer graph analysis using subgraph representation", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 1176635 (13 March 2021); https://doi.org/10.1117/12.2590988

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