Fusing Fine-Grained Information of Sequential News for Personalized News Recommendation

被引：1
|
作者：
Zhang, Jin-Cheng [1 ]
Zain, Azlan Mohd [1 ]
Zhou, Kai-Qing [2 ]
Chen, Xi [3 ]
Zhang, Ren-Min [2 ]
机构：
[1] Univ Teknol Malaysia, Fac Comp, Skudai, Malaysia
[2] Jishou Univ, Coll Informat Sci & Engn, Jishou, Peoples R China
[3] Tencent Inc, Shenzhen, Peoples R China
来源：
DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2023, PT II | 2023年 / 14147卷
基金：
中国国家自然科学基金;
关键词：
Fine-grained Information; News Recommendation; Personalized Attention;
D O I：
10.1007/978-3-031-39821-6_9
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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.
引用
收藏
页码：119 / 125
页数：7
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