News Sequence Recommendation Model with Dual-View Category Enhancement

被引：0

作者：

Li, Wenchao ^{[1
]}

Hu, Qiang ^{[1
]}

机构：

[1] Qingdao Univ Sci & Technol, Coll Informat Sci & Technol, Qingdao 266100, Peoples R China

来源：

ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PT III, ICIC 2024 | 2024年 / 14877卷

关键词：

News recommendation; Dual-view; Context-weighted; Multiple user interests;

D O I：

10.1007/978-981-97-5669-8_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Personalized news recommendation enriches readers' news consumption experience by aligning news articles with their preferences. However, existing methods often fall short in extracting complete news topic features and accommodating variations in reader preferences. To tackle these challenges, a dual-view category-enhanced news sequence recommendation model is proposed. In the news representation module, we enhance CNN-derived topic vectors with a context-weighted word-based approach, rectifying dispersed topic extraction. It yields a comprehensive news representation vector. In the user representation module, a graph construction method, utilizing a sliding window technique, captures temporal user-centric features and multiple user interests. Dual-view-learning ensures both temporal and category-based features are encoded effectively. Experiment results on real-world datasets indicate that the proposed model exhibits exceptional news recommendation performance, with significantly higher quality compared to the comparison models.

引用

页码：90 / 101

页数：12

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