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山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (2): 71-79.doi: 10.6040/j.issn.1672-3961.1.2016.099

• 机器学习与数据挖掘 • 上一篇    下一篇

面向推荐的用户兴趣扩展方法

王鑫1,2,陆静雅2,王英2*   

  1. 1. 长春工程学院计算机技术与工程学院, 吉林 长春 130012;2. 吉林大学符号计算与知识工程教育部重点实验室, 吉林 长春 130012
  • 收稿日期:2016-03-01 出版日期:2017-04-20 发布日期:2016-03-01
  • 通讯作者: 王英(1981— ),女,黑龙江哈尔滨人,副教授,博士,主要研究方向为社会计算、机器学习、数据挖掘. E-mail: wangying2010@jlu.edu.cn E-mail:xinwangjlu@gmail.com
  • 作者简介:王鑫(1981— ),男,辽宁葫芦岛人,讲师,博士,主要研究方向为社会计算、机器学习、数据挖掘. E-mail: xinwangjlu@gmail.com
  • 基金资助:
    国家自然科学基金资助项目(61602057);符号计算与知识工程教育部重点实验室开放基金资助项目(93K172016K13);吉林省科技厅优秀青年人才基金资助项目(20170520059JH);吉林省教育厅青年基金资助项目(2016311);广西可信软件重点实验室研究课题资助项目(kx201533)

Exploring user interest expansion method for recommendation

WANG Xin1,2, LU Jingya2, WANG Ying2*   

  1. 1. School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, Jilin, China;
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, Jilin, China
  • Received:2016-03-01 Online:2017-04-20 Published:2016-03-01

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摘要： 提出了一种用户兴趣扩展的方法以便应用于个性化推荐系统,对用户的搜索点击日志和浏览器的浏览日志进行统计,粗略对用户兴趣建模,从文本相似度、语言模型相关度、潜在的语义关联关系三个方面充分分析用户兴趣方向之间的关联关系,应用社区发现思想挖掘关联关系紧密的兴趣群组,并对用户兴趣在同一群组内进行适当扩展。通过试验结果分析,可以看出用户兴趣扩展对个性化推荐点击率的影响,并使点击率有近一倍的增长。

关键词: Infomap搜索算法, 映射公式, 兴趣扩展, 社区发现, 个性化推荐

Abstract: An approach of user interest expansion was presented and applied into personal recommendation system, the basic idea was to make some statistics on user's browsing log and clicking log, the user's interest was roughly modelled. The associated relationship from the text similarity, the relevance of language model and potential semantic relationship between the directions of user interest was analyzed, the interest groups using community detection method was identified, the user's interest was enriched appropriately in the same group. By experimental analysis, the impact of user's interest expansion on click rate in personalized recommendations was observed. The click rate had nearly doubled growth.

Key words: personalized recommendation, Infomap search algorithm, interest expansion, map equation, communities detection

中图分类号: 

  • TP391
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