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现代图书情报技术  2015, Vol. 31 Issue (1): 59-65    DOI: 10.11925/infotech.1003-3513.2015.01.09
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
社交网络中的好友推荐方法研究
吴昊, 刘东苏
西安电子科技大学经济与管理学院 西安 710126
Friend Recommendation in Social Network
Wu Hao, Liu Dongsu
School of Economics & Management, Xidian University, Xi'an 710126, China
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摘要 

[目的] 利用社交网络中用户的好友和历史行为, 为用户推荐潜在的好友。[方法] 通过共同好友比例和互动比例两个指标衡量社交网络图中好友关系亲密程度, 综合社交兴趣度和兴趣相似度进行评分, 选取分数最高的Top-k用户推荐给目标用户。[结果] 实验结果表明, 相比传统方法, 本文方法在准确率和召回率上均有显著提升。[局限] 互动行为中的非正常情况未识别和处理, 可能影响推荐结果准确率。[结论] 考虑互动比例等多因素的好友推荐方法较传统单一角度方法有更好的效果。

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关键词 社交网络好友推荐兴趣相似度互动    
Abstract

[Objective] Make use of the friends and historical behavior of users in social network, to recommend potential friends for the target users. [Methods] The proportion of common friends and the proportion of interaction are used as indicators to measure the closeness of the relationship in a social network graph. The relationship between friends is scored according to sociality interest and interest similarity, and the Top-k users with the highest scores are recommended to the target users. [Results] Experimental results show that the precision rate and recall rate of this method are improved significantly in comparison with traditional methods. [Limitations] Abnormal interaction without identification and treatment, may affect the accuracy of the recommendation results. [Conclusions] Considering more factors, including the proportion of interaction, the improved friend recommendation method has a better effect than traditional single factor method.

Key wordsSocial network    Friend recommendation    Interest similarity    Interaction
收稿日期: 2014-07-17     
:  G354  
通讯作者: 吴昊,ORCID:0000-0003-0113-3019,E-mail:arrow018@qq.com。     E-mail: arrow018@qq.com
作者简介: 作者贡献声明: 吴昊: 提出研究思路, 实施实验, 起草论文; 刘东苏: 论文最终版本修订。
引用本文:   
吴昊, 刘东苏. 社交网络中的好友推荐方法研究[J]. 现代图书情报技术, 2015, 31(1): 59-65.
Wu Hao, Liu Dongsu. Friend Recommendation in Social Network. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2015.01.09.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.01.09

[1] 何静, 郭进利, 徐雪娟. 微博用户行为统计特性及其动力学分析[J]. 现代图书情报技术, 2013(7-8): 94-100. (He Jing, Guo Jinli, Xu Xuejuan. Analysis on Statistical Characteristic and Dynamics for User Behavior in Microblog Communities [J]. New Technology of Library and Information Service, 2013(7-8): 94-100.)
[2] 项亮. 推荐系统实践[M]. 北京: 人民邮电出版社, 2012. (Xiang Liang. Recommendation System Practice [M]. Beijing: Posts & Telecom Press, 2012.)
[3] Massa P, Bhattacharjee B. Using Trust in Recommender Systems: An Experimental Analysis [C]. In: Proceedings of the 2nd International Conference on Trust Management (iTrust 2004), Oxford, UK. Springer Berlin Heidelberg, 2004: 221-235.
[4] Lo S, Lin C. WMR-A Graph-Based Algorithm for Friend Recommendation [C]. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, Hong Kong, China. IEEE Computer Society, 2006.
[5] Chin A. Finding Cohesive Subgroups and Relevant Members in the Nokia Friend View Mobile Social Network [C]. In: Proceedings of International Conference on Computational Science and Engineering (CSE'09), Vancouver, BC, Canada. IEEE, 2009: 278-283.
[6] Shen D, Sun J T, Yang Q, et al. Latent Friend Mining from Blog Data [C]. In: Proceedings of the 6th International Conference on Data Mining (ICDM'06), Hong Kong, China. IEEE, 2006: 552-561.
[7] Zheng Y, Chen Y, Xie X, et al. GeoLife2.0: A Location-Based Social Networking Service [C]. In:Proceedings of the 10th International Conference on Mobile Data Management: Systems, Services and Middleware, Taipei, China. IEEE, 2009: 357-358.
[8] Bacon K, Dewan P. Towards Automatic Recommendation of Friend Lists [C]. In:Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing, Washington, DC, US. IEEE, 2009: 1-5.
[9] Wu Z, Jiang S, Huang Q. Friend Recommendation According to Appearances on Photos [C]. In: Proceedings of ACM International Conference on Multimedia, Beijing, China. ACM, 2009.
[10] 牛庆鹏. 博客潜在朋友推荐技术的研究[D]. 沈阳: 东北大学, 2009. (Niu Qingpeng. Research on Blog Friends Recommendation Mechanism in Blogsphere [D]. Shenyang: Northeastern University, 2009.)
[11] 于海群, 刘万军, 邱云飞. 基于用户偏好的社会网络二级人脉推荐研究[J]. 计算机应用与软件, 2012, 29(4): 39-43. (Yu Haiqun, Liu Wanjun, Qiu Yunfei. Second-level Contacts Recommendation of SNS Based on Users Preferences [J]. Computer Applications and Software, 2012, 29(4): 39-43.)
[12] 史岭峰. 基于社交网络好友关系的图查询算法研究与应用[D]. 南京: 南京理工大学, 2012. (Shi Lingfeng. Research and Application of Graph Query Algorithm Based on Friends Relationship in Social Network [D]. Nanjing: Nanjing University of Science & Technology, 2012.)
[13] 刘乾. 基于社交网络和地理位置信息的好友推荐方法研究[D]. 杭州: 浙江大学, 2013. (Liu Qian. Friend Recommendation Based on Social Network and Location Information [D]. Hangzhou: Zhejiang University, 2013.)
[14] 杨婷. 基于MapReduce的好友推荐系统的研究与实现[D]. 北京: 北京邮电大学, 2013. (Yang Ting. Research and Implementation of Recommendation System Based on MapReduce [D]. Beijing: Beijing University of Posts and Telecommunications, 2013.)
[15] 施少怀. 一种基于用户倾向的微博好友推荐算法[D]. 哈尔滨: 哈尔滨工业大学, 2013. (Shi Shaohuai. A Micro Blog Friend Recommendation Algorithm Based on User Tendency [D]. Harbin: Harbin Institute of Technology, 2013.)
[16] Chen J, Geyer W, Dugan C, et al. Make New Friends, but Keep the Old: Recommending People on Social Networking Sites [C]. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems. New York, NY, USA:ACM, 2009: 201-210.
[17] 胡文江, 胡大伟, 高永兵, 等. 基于关联规则与标签的好友推荐算法[J]. 计算机工程与科学, 2013, 35(2): 109-113. (Hu Wenjiang, Hu Dawei, Gao Yongbing, et al. Friend Recommendation Algorithm Based on Association Rules and Tags [J]. Computer Engineering & Science, 2013, 35(2): 109-113.)
[18] 张中峰, 李秋丹. 社交网站中潜在好友推荐模型研究[J]. 情报学报, 2011, 30(12): 1319-1325. (Zhang Zhongfeng, Li Qiudan. Latent Friend Recommendation in Social Network Services [J]. Journal of the China Society for Scientific and Technical Information, 2011, 30(12): 1319-1325.)
[19] 杨晶, 杨长春, 丁虹. 一种改进的新浪微博好友推荐算法[J]. 常州大学学报: 自然科学版, 2013, 25(3): 66-70. (Yang Jing, Yang Changchun, Ding Hong. A Modified Friend Recommending Algorithm Based on Sina Microblogging [J]. Journal of Changzhou University:Natural Science Edition, 2013, 25(3): 66-70.)
[20] 黄亮, 杜永萍. 基于信任关系的潜在好友推荐方法[J]. 山东大学学报: 理学版, 2013, 48(11): 73-79. (Huang Liang, Du Yongping. The Method of Latent Friend Recommendation Based on the Trust Relations [J]. Journal of Shandong University: Nature Science, 2013, 48(11): 73-79.)
[21] Moricz M, Dosbayev Y, Berlyant M. PYMK: Friend Recommendation at Myspace [C]. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, Indianapolis, Indiana, USA. New York, NY, USA: ACM, 2010: 999-1002.
[22] 郑佳佳. 社交网络中基于图排序的好友推荐机制研究与实现[D]. 杭州: 浙江大学, 2011. (Zheng Jiajia. Friends Recommendation Based on Graph Ranking on Social Network Site [D]. Hangzhou: Zhejiang University, 2011.)
[23] ACM SIGKDD. KDD Cup 2012 Track 1 Data [DB/OL]. [2014-05-10]. http://www.kddcup2012.org/c/kddcup2012-track1/data.

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