News Recommendation with Attention Mechanism
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910336382140416 |
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| author | Liu, Tianrui Xu, Changxin Qiao, Yuxin Jiang, Chufeng Chen, Weisheng |
| author_facet | Liu, Tianrui Xu, Changxin Qiao, Yuxin Jiang, Chufeng Chen, Weisheng |
| contents | This paper explores the area of news recommendation, a key component of online information sharing. Initially, we provide a clear introduction to news recommendation, defining the core problem and summarizing current methods and notable recent algorithms. We then present our work on implementing the NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and assess its effectiveness. Our evaluation shows that NRAM has the potential to significantly improve how news content is personalized for users on digital news platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07422 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | News Recommendation with Attention Mechanism Liu, Tianrui Xu, Changxin Qiao, Yuxin Jiang, Chufeng Chen, Weisheng Artificial Intelligence This paper explores the area of news recommendation, a key component of online information sharing. Initially, we provide a clear introduction to news recommendation, defining the core problem and summarizing current methods and notable recent algorithms. We then present our work on implementing the NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and assess its effectiveness. Our evaluation shows that NRAM has the potential to significantly improve how news content is personalized for users on digital news platforms. |
| title | News Recommendation with Attention Mechanism |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2402.07422 |