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山东大学学报（理学版） ›› 2017, Vol. 52 ›› Issue (1): 15-22.doi: 10.6040/j.issn.1671-9352.1.2015.118

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基于用户隐式兴趣模型的信息推荐

杨震1,2,3,司书勇1,李超阳1   

  1. 1. 北京工业大学计算机学院, 北京 100124;2. 可信计算北京市重点实验室, 北京 100124;3. 桂林电子科技大学, 广西高校云计算与复杂系统重点实验室, 广西 桂林 541004
  • 收稿日期:2015-11-14 出版日期:2017-01-20 发布日期:2017-01-16
  • 作者简介:杨震(1979— ), 男, 博士,副教授, 研究方向为数据挖掘、模式识别、内容安全方面研究. E-mail: yangzhen@bjut.edu.cn
  • 基金资助:
    广西高校云计算与复杂系统重点实验室资助项目(15205)

Information recommendation based on users interest model

YANG Zhen1,2,3, SI Shu-yong1, LI Chao-yang1   

  1. 1. College of Computer Science, Beijing University of Technology, Beijing 100124, China;
    2. Beijing Key Laboratory of Trusted Computing, Beijing 100124, China;
    3. Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China
  • Received:2015-11-14 Online:2017-01-20 Published:2017-01-16

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摘要： 信息推荐技术能够帮助用户从海量网络信息中提取有用信息,因而得到研究者的广泛关注。通过建立用户隐式特征兴趣模型,即将用户-行为矩阵分解为用户-隐式兴趣-行为矩阵,在充分挖掘用户隐式兴趣的基础上,研究并实现了基于隐式特征兴趣模型的协同过滤算法。在Movielens语料集上进行测试的结果表明,隐式特征能够更加精准地表述用户兴趣,有效提升信息推荐性能。

关键词: 用户兴趣模型, 隐语义模型, 信息推荐, 个性化推荐, 协同过滤

Abstract: Information recommendation technology can help users filtering out useful content from the huge amount of information on the Internet, thus attracts a wide range of researchers attention. In this paper, we proposed a collaborative recommendation algorithm based on the users interest by using latent factor model, which captured the users implicit interests by decompose the User-Behavior matrix into a product of a User-Implicit matrix and an Interest-Behavior matrix. The experimental results in the MovieLens data sets show that the implicit characteristic can reflect the users interest more precisely than explicit characteristics, as a result, improving the recommendation performance as an expectation.

Key words: information recommendation, users interest model, latent factor model, collaborative filtering, personalized recommendation

中图分类号: 

  • TP391
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