图书情报工作 ›› 2019, Vol. 63 ›› Issue (2): 99-106.DOI: 10.13266/j.issn.0252-3116.2019.02.012

• 情报研究 • 上一篇    下一篇

基于用户决策机理的个性化推荐

林鑫1,2, 桑运鑫3, 龙存钰2   

  1. 1. 中国科学技术信息研究所 北京 100038;
    2. 华中师范大学信息管理学院 武汉 430079;
    3. 武汉大学信息管理学院 武汉 430072
  • 收稿日期:2018-02-11 修回日期:2018-06-11 出版日期:2019-01-20 发布日期:2019-01-20
  • 通讯作者: 龙存钰(ORCID:0000-0001-5096-7461),本科生,通讯作者,E-mail:345705868@qq.com
  • 作者简介:林鑫(ORCID:0000-0003-0318-8160),讲师,博士;桑运鑫(ORCID:0000-0002-0026-4319),本科生。
  • 基金资助:
    本文系国家社会科学基金青年项目"社会网络中基于用户认知结构的知识标注研究"(项目编号:17CTQ024)研究成果之一。

Personalized Recommendation Based on User Decision-making Mechanism

Lin Xin1,2, Sang Yunxin3, Long Cunyu2   

  1. 1. Institute of Scientific and Technical Information of China, Beijing 100038;
    2. School of Information Management of Central Normal University, Wuhan 430079;
    3. School of Information Management of Wuhan University, Wuhan 430072
  • Received:2018-02-11 Revised:2018-06-11 Online:2019-01-20 Published:2019-01-20

摘要: [目的/意义]针对基于内容的个性化推荐策略,提出资源特征选择与权值计算优化策略,从而改善个性化推荐的效果。[方法/过程]构建基于用户决策机理的个性化推荐模型,模型以用户决策机理为背景知识进行资源特征的选择、用户兴趣模型的构建与语义表示、用户决策函数构建。为验证模型效果,以4 748位用户的观影数据为例进行实验,实验以向量空间模型为参照模型,P@N为评价指标。[结果/结论]实验结果显示,在N取值为5、10、20、50、100、200的情况下,基于用户决策机理的个性化推荐模型效果都显著优于向量空间模型,从而验证模型的有效性。

关键词: 决策机理, 基于内容推荐, 个性化推荐

Abstract: [Purpose/significance] The purpose of this paper is to propose an optimization strategy of features choosing and weight computing for content-based personalized recommendation.[Method/process] This paper proposes a personalized recommendation model based on user's decision-making mechanism, which takes user decision mechanism as background knowledge in features selection, user interest profile construction and semantic representation, and user decision function construction. To test this model, this paper conducts an experiment taking 4 748 users as sample, vector space model as reference model, and P@N as evaluation index.[Result/conclusion] The results show that, in the cases of N equals 5, 10, 20, 50, 100, 200, the personalized recommendation model based on user decision-making mechanism is significantly better than the vector space model, and the effectiveness of the model is verified.

Key words: decision-making mechanism, content-based recommendation, personalized recommendation

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