A Neural Inference of User Social Interest for Item Recommendation
Abstract User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information for recommendation tasks. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bo...
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
SpringerOpen
2023-08-01
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| Collection: | Data Science and Engineering |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/s41019-023-00225-8 |
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