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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| Format: | Conference or Workshop Item |
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2023
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| _version_ | 1817854253376471040 |
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| author | Zhang, Jin Cheng Mohd. Zain, Azlan Zhou, Kai Qing Chen, Xi Zhang, Ren Min |
| author_facet | Zhang, Jin Cheng Mohd. Zain, Azlan Zhou, Kai Qing Chen, Xi Zhang, Ren Min |
| author_sort | Zhang, Jin Cheng |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-12-08T06:55:12Z |
| format | Conference or Workshop Item |
| id | utm.eprints-108154 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-12-08T06:55:12Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | utm.eprints-1081542024-10-20T08:02:16Z http://eprints.utm.my/108154/ Fusing fine-grained information of sequential news for personalized news recommendation Zhang, Jin Cheng Mohd. Zain, Azlan Zhou, Kai Qing Chen, Xi Zhang, Ren Min QA75 Electronic computers. Computer science 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. 2023 Conference or Workshop Item PeerReviewed Zhang, Jin Cheng and Mohd. Zain, Azlan and Zhou, Kai Qing and Chen, Xi and Zhang, Ren Min (2023) Fusing fine-grained information of sequential news for personalized news recommendation. In: The 34th International Conference on Database and Expert Systems Applications DEXA 2023, 28 August 2023 - 30 August 2023, Penang, Malaysia. http://dx.doi.org/10.1007/978-3-031-39821-6_9 |
| spellingShingle | QA75 Electronic computers. Computer science Zhang, Jin Cheng Mohd. Zain, Azlan Zhou, Kai Qing Chen, Xi Zhang, Ren Min Fusing fine-grained information of sequential news for personalized news recommendation |
| title | Fusing fine-grained information of sequential news for personalized news recommendation |
| title_full | Fusing fine-grained information of sequential news for personalized news recommendation |
| title_fullStr | Fusing fine-grained information of sequential news for personalized news recommendation |
| title_full_unstemmed | Fusing fine-grained information of sequential news for personalized news recommendation |
| title_short | Fusing fine-grained information of sequential news for personalized news recommendation |
| title_sort | fusing fine grained information of sequential news for personalized news recommendation |
| topic | QA75 Electronic computers. Computer science |
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