Fusing fine-grained information of sequential news for personalized news recommendation
In this paper, we propose a novel method that fuses Fine-grained Information of Sequential News for personalized news recommendation (FISN). FISN comprises three primary modules: news encoder, clicked news optimizer and user encoder. The news encoder uses fine-grained information to learn accurate n...
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| Main Authors: | , , , , |
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| 格式: | Conference or Workshop Item |
| 出版: |
2023
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| 主題: | |
| 在線閱讀: | http://eprints.utm.my/108154/ http://dx.doi.org/10.1007/978-3-031-39821-6_9 |
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| 總結: | In this paper, we propose a novel method that fuses Fine-grained Information of Sequential News for personalized news recommendation (FISN). FISN comprises three primary modules: news encoder, clicked news optimizer and user encoder. The news encoder uses fine-grained information to learn accurate news representations. The clicked news optimizer introduces multi-headed self-attention and positional encoding techniques to optimize the clicked news representation. The user encoder uses news-level attention to learn user representations. Extensive experimental results demonstrate that FISN outperforms many baseline approaches in terms of metrics for real datasets. |
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