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Hybrid news recommendation algorithm combining social relation and tag information

Xia Hongbin1,2
Liu Chunqin1
Liu Yuan1,2
1. School of Digital Media, Jiangnan University, Wuxi Jiangsu 214122, China
2. Jiangsu Key Laboratory of Media Design & Software Technology, Wuxi Jiangsu 214122, China

Abstract

Concerning the problem that data sparsity and user preferences quickly change for traditional news recommendation, this paper proposed a hybrid news recommendation algorithm combining social relations and tag information. Firstly, the algorithm utilized the social relationship and hashtag information in the user′s social network. Then it applied the LDA topic model to model user interest. Finally, the algorithm used a hybrid recommendation algorithm based on content and collaborative filtering to complete news recommendations. In the experiments, comparing with existing recommendation algorithms, the proposed algorithm can improve the precision by 10.7%, MRR by 4.1%, NDCG by 10%. The proposed algorithm can improve the accuracy and the quality of news recommendation algorithm effectively.

Foundation Support

国家自然科学基金资助项目(61672264)
国家科学支撑计划课题(2015BAH54F01)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.10.0598
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 1
Section: Algorithm Research & Explore
Pages: 61-64
Serial Number: 1001-3695(2021)01-012-0061-04

Publish History

[2021-01-05] Printed Article

Cite This Article

夏鸿斌, 刘春芹, 刘渊. 融合社交关系和标签信息的混合新闻推荐算法 [J]. 计算机应用研究, 2021, 38 (1): 61-64. (Xia Hongbin, Liu Chunqin, Liu Yuan. Hybrid news recommendation algorithm combining social relation and tag information [J]. Application Research of Computers, 2021, 38 (1): 61-64. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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