| Autor: |
Taisei Hirakawa, Satoshi Asamizu, Miki Haseyama, Keisuke Maeda, Takahiro Ogawa |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
GCCE |
| DOI: |
10.1109/gcce50665.2020.9292019 |
| Popis: |
This paper presents cross-domain recommendation via multi-layer graph analysis using user-item embedding. The proposed method constructs two graphs in source and target domains utilizing user-item embedding, respectively. By training relationship between the user’s embedding features in these two graphs, the proposed method realizes the cross-domain recommendation via the multi-layer graphs. This is the main contribution of this paper. Then the proposed method can estimate the user’s embedding in the target domain from that in the source domain. Finally, our method can recommend items to users via the estimated user’s embedding. Experiments on real-world datasets verify the effectiveness of the proposed method. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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