Algorithm Research & Explore
|
2956-2960

Recommendation supplemental network of neighbor reviews

Feng Xingjie
Zeng Yunze
Cui Guiying
School of Computer Science & Technology, Civil Aviation University of China, Tianjin 300300, China

Abstract

Review data has sparse problems that are insufficient to support learning more comprehensive user preferences. Focusing on this issue, this paper proposed a plug and play auxiliary network(NRSN), which can be combined with different models to improve their performance. The network mainly re-adjusts the user preference vector of the current model output by adding auxiliary information. Firstly, according to the target user, it used the aspect-attention mechanism to learn the preference of the neighboring users from the reviews of their neighbors. Then, it used the co-attention mechanism to match the neighboring users and the target users, and adjusted the new preference vector of the target users. The experiment on three public datasets result show that NRSN can not only improve the performance of existing models, but also can effectively alleviate the impact of review sparseness in "cold start" scenario.

Foundation Support

中央高校基本科研业务费资助项目(201921)
国家自然科学基金委员会与中国民用航空局联合基金项目(U1233113,U1633110)
国家自然科学青年基金资助项目(61301245,61201414)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.05.0191
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 10
Section: Algorithm Research & Explore
Pages: 2956-2960
Serial Number: 1001-3695(2020)10-015-2956-05

Publish History

[2020-10-05] Printed Article

Cite This Article

冯兴杰, 曾云泽, 崔桂颖. 基于近邻用户评论的推荐辅助网络 [J]. 计算机应用研究, 2020, 37 (10): 2956-2960. (Feng Xingjie, Zeng Yunze, Cui Guiying. Recommendation supplemental network of neighbor reviews [J]. Application Research of Computers, 2020, 37 (10): 2956-2960. )

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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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