计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 482-485.doi: 10.11896/jsjkx.200400028

• 大数据&数据科学 • 上一篇    下一篇

融合语义模型的二分网络推荐算法

周波   

  1. 中国科学院科技战略咨询研究院 北京 100190
  • 出版日期:2020-11-15 发布日期:2020-11-17
  • 通讯作者: 周波(806446828@qq.com)
  • 作者简介:806446828@qq.com

Bipartite Network Recommendation Algorithm Based on Semantic Model

ZHOU Bo   

  1. Institute of Science and Development,Chinese Academy of Sciences,Beijing 100190,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:ZHOU Bo,born in 1991,master.His main research interests include data mining,and intelligence analysis.

摘要: 当前基于二分网络的推荐算法未考虑推荐对象之间的语义关系,因此文中提出一种融合语义模型的二分网络推荐算法。该算法利用作者主题模型将推荐对象的语义信息降维至二维向量空间;然后计算推荐对象之间的语义相似度,把该语义相似度融合到基于物质扩散的二分网络推荐算法中。以新能源汽车专利权人推荐为实例进行实验验证,结果表明,该算法相比于单一的二分网络推荐算法具有更高的准确率和召回率,准确率提高比率为2.29%,召回率提高比率为4.15%。

关键词: 语义模型, 作者主题模型, 二分网络, 推荐算法

Abstract: The current research of bipartite network recommendation algorithm does not consider the semantic relationship,so this paper proposes an improved bipartite network recommendation algorithm.Author topic model (AT model) is used to embed the semantic information into a two dimensions semantic space.Then the semantic similarity between the recommended objects is calculated and integrated into the similarity calculation of bipartite network recommendation algorithm.The algorithm is verified by the recommendation of the new energy vehicle patentee.Experimental results show that the new algorithm has higher accuracy and recall rate than the bipartite network recommendation algorithm,the accuracy rate is increased by 2.29%,the recall rate is increased by 4.15%.

Key words: Semantic model, Author topic model, Bipartite network, Recommendation algorithm

中图分类号: 

  • TP391
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