计算机应用 ›› 2017, Vol. 37 ›› Issue (3): 791-795.DOI: 10.11772/j.issn.1001-9081.2017.03.791

• 人工智能 • 上一篇    下一篇

结合评分和信任关系的社会化推荐算法

胡云1, 李慧2, 施珺2   

  1. 1. 南京中医药大学 信息技术学院, 南京 210023;
    2. 淮海工学院 计算机工程学院, 江苏 连云港 222001
  • 收稿日期:2016-09-26 修回日期:2016-10-11 出版日期:2017-03-10 发布日期:2017-03-22
  • 通讯作者: 李慧
  • 作者简介:胡云(1978-),女,江苏连云港人,副教授,博士,主要研究方向:社区发现技术、社会网络分析;李慧(1979-),女,江苏连云港人,副教授,博士,主要研究方向:个性化推荐、社会网络分析;施珺(1963-),女,安徽桐城人,教授,硕士,主要研究方向:智慧教育。
  • 基金资助:
    国家自然科学基金资助项目(61403156,61403155);连云港市科技计划项目(SH1507,CXY1530,CG1315,CG1413)。

Social recommendation algorithm combining rating and trust relation

HU Yun1, LI Hui2, SHI Jun2   

  1. 1. College of Information Technology, Nanjing University of Chinese Medicine, Nanjing Jiangsu 210023, China;
    2. School of Computer Engineering, Huaihai Institute of Technology, Lianyungang Jiangsu 222001, China
  • Received:2016-09-26 Revised:2016-10-11 Online:2017-03-10 Published:2017-03-22
  • Supported by:
    This work is partially supported by the National Natural Scienece Foundation of China (61403156, 61403155), Lianyungang Science and Technology Project (SH1507, CXY1530, CG1315, CG1314).

摘要: 针对推荐系统中普遍存在的数据稀疏和冷启动等问题,提出一种综合评分和信任关系的社会化推荐算法。首先对网络中新用户的初始信任值进行合理赋值,有效地解决了新用户的信任冷启动问题。鉴于用户的喜好会受其朋友的影响,推荐模型又利用朋友之间的信任矩阵对用户自身的特征向量进行修正,解决了用户特征向量的精准构建及信任传递问题。实验结果表明,所提算法较传统的社会网络推荐算法在性能上有显著提高。

关键词: 信任, 推荐, 传递, 模型, 矩阵分解

Abstract: To solve the problem of data sparsity and cold start which is prevalent in recommender system, a new social recommendation algorithm was proposed, which integrates rating and trust relation. Firstly, the initial trust value of the new user in the network was reasonably assigned, which solves the problem of cold start of the new user. Since the user's preferences were affected by his friends, the user's own feature vector was modified by the trust matrix between friends, which solves the problem of user's feature vector construction and trust transition. The experimental results show that the proposed algorithm has a significant performance improvement over the traditional social network recommendation algorithm.

Key words: trust, recommendation, transition, model, matrix factorization

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