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中国管理科学

中国管理科学 ›› 2017, Vol. 25 ›› Issue (3): 164-171.doi: 10.16381/j.cnki.issn1003-207x.2017.03.019

• 论文 • 上一篇    下一篇

基于用户社会关系的社交网络好友推荐算法研究

景楠1, 王建霞1, 许皓2, 卞亦文1   

  1. 1. 上海大学悉尼工商学院, 上海 201899;
    2. 安徽大学商学院, 安徽 合肥 230601
  • 收稿日期:2015-11-18 修回日期:2016-05-14 出版日期:2017-03-20 发布日期:2017-05-27
  • 通讯作者: 卞亦文(1978-),男(汉族),安徽芜湖人,上海大学悉尼工商学院,博士,教授,研究方向:服务运作管理、决策分析,E-mail:ywbian@shu.edu.cn.
  • 基金资助:

    国家自然科学基金面上资助项目（71371010，71571115）；上海市科学委员会科技人才计划项目（14PJ1403700）；上海市教育委员会科研创新项目（14YS006）；教育部在线教育研究中心在线教育研究基金（全通教育）项目资助（2016YB138）

Friend Recommendation Algorithm based on User Relations in Social Networks

JING Nan1, WANG Jian-xia1, XU Hao2, BIAN Yi-wen1   

  1. 1. SHU-UTS SILC Business School, Shanghai University, Shanghai 201899, China;
    2. Schoolof Business, Anhui University, Hefei 230601, China
  • Received:2015-11-18 Revised:2016-05-14 Online:2017-03-20 Published:2017-05-27

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被引次数

1

摘要： 社交网络中存在海量用户，如何有效推荐好友是社交网络可持续发展的重要环节，也是社交网络相关研究的重要主题。当前实践及现有研究往往基于用户的显性信息推荐好友，而忽略了用户之间的隐性社会关系；此外，显性信息往往不够完整且存在虚假信息问题。为有效实现好友推荐，本文提出了基于用户社会关系的好友推荐算法，并重点应用关联规则算法分析用户之间的隐含关联度，构造用户之间的网络有向图及关系转移矩阵；然后，结合关系转移矩阵与PageRank算法计算每个用户的分数，将分数较高的用户推荐给目标用户。在此基础上，本文引入用户影响力，提出综合考虑用户社会关系及用户影响力的PeopleRank算法。为验证算法的合理性和有效性，将本文所提出的两种算法与传统的社会过滤算法、PageRank算法进行对比分析。为此，本文抓取了Twitter社交网站上用户数据开展实验分析。实验结果显示本文所提出的算法具有较好的推荐效果，尤其是综合考虑用户社会关系及用户影响力的好友推荐算法在推荐准确率和推荐召回率上都有明显的优势。

关键词: 社交网络, 好友推荐, 社会关系, 影响力, PageRank算法

Abstract: Due to the vast amounts of users, it is difficult for a user to make effective connections with others for common interests. Friend recommendation on online social networks, therefore, becomes a challenging research issue, which may have significant effects on sustainable developments of social networks.Most of the existing friend recommendation methods are conducted based on users' explicit information such as background, demography, interests and posts, while ignoring users' implicit information such as their social relationships. Notably, explicit information is often incomplete and not trustworthy, and cannot be appropriately used to measure user similarities.In order to effectively recommend friends, a recommendation algorithm is proposed based on user relationship information in online social networks. In the described algorithm, user relationships are characterized by using the association analysis method, and then a weighted, directed graph between network users is constructed. Based on this graph, this algorithm builds a transition matrix and uses the PageRank algorithm to calculateusers' scores that indicate the acceptance probabilities, and then recommend the users with high scores to the target user on social networks. In addition, with the consideration of the user authority in a specific social network, an enhanced friend recommendation algorithm is further developed.In order to validate the proposed approaches, friend recommendation experiments on Twitter are conducted and the users' information and their relationship data are extracted. For this purpose, two traditional methods, i.e., social filtering algorithm and the PageRank algorithm, are used to compare with the two proposed approaches based on two measures, i.e., accuracy and recall rate. Experiments results show that the proposed recommendation algorithms yield clearly better results in accuracy and recall rate than the traditional recommendation algorithms.

Key words: social networks, friend recommendation, social relationship, user authority, PageRank algorithm

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