Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (9): 2386-2389.DOI: 10.11772/j.issn.1001-9081.2016.09.2386

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Friend recommendation method for mobile social networks

WANG Shanshan, LENG Supeng   

  1. College of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, China
  • Received:2016-02-17 Revised:2016-05-05 Online:2016-09-10 Published:2016-09-08
  • Supported by:
    This work is partially supported by the Fundamental Research Funds for the Central Universities (ZYGX2013J009) and the Seventh Framework Project Proposed by European Union (EU FP7 Project CLIMBER:PIRSESGA-2012-318939), the Information Technology Research Projects of Ministry of Transport of China (2014364X14040).


王珊珊, 冷甦鹏   

  1. 电子科技大学 通信与信息工程学院, 成都 611731
  • 通讯作者: 冷甦鹏
  • 作者简介:王珊珊(1990-),女,浙江慈溪人,硕士研究生,主要研究方向:移动社会网络、服务器集群;冷甦鹏(1973-),男,四川资中人,教授,博士生导师,博士,主要研究方向:物联网、无线传感网、下一代宽带无线网络、移动社会网络、智能交通。
  • 基金资助:

Abstract: In view of the friend recommendation in Mobile Social Network (MSN), a new method based on multi-dimensional similarity was proposed. The method is a kind of method based on content, but not confined to single dimension matching information, it judges users' similarity of various dimensions from three aspects of space, time and interest, then gets a comprehensive judgment by "difference distance". The proposed method can recommend other users to target audience when they are consistent in geographical position, online-time and interest. The experimental results show that when the method is used in the friend recommendation of mobile social networks, its precision and efficiency are up to 80% and 60% respectively, the performance is much better than the other friend recommendation methods based on single dimension; at the same time, by adjusting the value of three dimensional weights, the method can be used in a variety of mobile social networks with different characteristics.

Key words: Mobile Social Network (MSN), personalized service, friend recommendation, multidimensional, similarity

摘要: 针对移动社会网络(MSN)的好友推荐问题,提出了一种基于多维相似度的好友推荐方法。该方法隶属于基于内容的好友推荐,但与现有方法相比,不再局限于单一维度的匹配信息,而是从空间、时间和兴趣三个维度出发,判断用户在各个维度上的相似度,最终通过“差异距离”进行综合评判,向目标用户推荐与之在地理位置、在线时间和兴趣爱好上更具一致性的其他用户成为其好友。由实验结果表明,该方法应用于移动社会网络中的好友推荐服务时,其推荐结果查准率接近80%,查准效率接近60%,性能远高于只基于单一维度的好友推荐方法;同时,通过对三维权重值的调整,该方法可应用于多种特性的移动社会网络中。

关键词: 移动社会网络, 个性化服务, 好友推荐, 多维度, 相似度

CLC Number: