计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 433-436.

• 大数据与数据挖掘 • 上一篇    下一篇

一种高效的社交网络朋友推荐方案

程宏兵1,王珂2,李兵2,钱漫匀1   

  1. 浙江工业大学计算机科学与技术学院 杭州3100231
    国家电网浙江省电力有限公司金华供电公司 浙江 金华3210002
  • 出版日期:2018-06-20 发布日期:2018-08-03
  • 作者简介:程宏兵(1979-),男,博士后,主要研究方向为网络与信息安全,E-mail:chenghb@zjut.edu.cn(通信作者);王 珂(1974-),女,硕士,主要研究方向为电网数据综合应用分析;李 兵(1972-),男,硕士,主要研究方向为电网数据分析技术;钱漫匀(1993-),女,硕士生,主要研究方向为社交网络与云安全。
  • 基金资助:
    国家自然科学基金项目(61402413)资助

Efficient Friend Recommendation Scheme for Social Networks

CHENG Hong-bing1,WANG Ke2,LI Bing2,QIAN Man-yun1   

  1. College of Computer Science &Technology,Zhejiang University of Technology,Hangzhou 310023,China1
    Jinhua Branch of Zhejiang Power Company,National Grid,Jinhua,Zhejiang 321000,China2
  • Online:2018-06-20 Published:2018-08-03

摘要: 当今社会,人们越来越多地通过社交网络来发言、聊天、交友。在互动过程中,除了用户主动关注感兴趣的人之外,社交网络也会为其推荐朋友。然而,所推荐的朋友大部分只是社交网络的推广,不一定符合用户的兴趣。针对社交网络推荐朋友的随机性和不可靠等问题,研究并提出了一种基于用户兴趣标签匹配的高效朋友推荐方案。首先,通过Word2Vec来训练语料库中的关键词,得到每个关键词的向量,产生一个词向量空间。其次,利用余弦相似度技术计算关键词之间的相似度并通过实验进行比较。实验中,综合选取合适的相似度值作为两个词向量是否相似的判断阈值。最后,将选取的相似度阈值应用到所提出的朋友兴趣匹配推荐算法中,并进行性能测试和各方案的仿真比较。结果表明,所提出的方案可靠且准确。

关键词: Word2Vec, 朋友推荐, 社交网络, 相似度计算

Abstract: With the rapid development of modern network technology,human society has entered the era of information.An increasing number of people prefer to talk and make friends with others through social networks.Besides the people or events which users initiatively focus on,social network will also recommend alternative users.However,most of the alternative users are the promotion of social networks.In this paper,for the accuracy and reliability of social networks recommendation,a new scheme based on tag matching was proposed.First,each word in the corpus is trained by Word2Vec,and then a word vectors space can be obtained and the similarity among words can be obtained by using the cosine similarity.Secondly,through the similarity comparison experiments,this paper chose an appropriate similarity value as the threshold to judge whether two words are similar.Finally,the similarity threshold was applied to the matching algorithm.The simulation experiments show that the recommend users are relatively reliable and accurate.

Key words: Friend recommendation, Similarity degree computing, Social networks, Word2Vec

中图分类号: 

  • TP393.0
[1]YIN Z,GUPTA M,WENINGER T,et al.LINKREC:a unified framework for link recommendation with user attributes and graph structure[C]∥International Conference on World Wide Web(WWW 2010).Raleigh,North Carolina,USA,DBLP,2010:1211-1212.
[2]YANG S H,LONG B,SMOLA A,et al.Like like alike:joint friendship and interest propagation in social networks[C]∥International Conference on World Wide Web.2011:537-546.
[3]GONG N Z,TALWALKAR A,MACKEY L,et al.Jointly Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)[C]∥The 6th SNA-KDD Workshop(SNA-KDD’12).Beijing,China,2012.
[4]WANG G,LIU Q,LI F,et al.Outsourcing privacy-preserving social networks to a cloud[C]∥IEEE INFOCOM.IEEE,2013:2886-2894.
[5]SHISHODIA M S,JAIN S,TRIPATHY B K.GASNA:greedy algorithm for social network anonymization[C]∥IEEE/ACM International Conference on Advances in Social Networks Anal-ysis and Mining.IEEE,2013:1161-1166.
[6]KHALID O,KHAN M U S,KHAN S U,et al.OmniSuggest:A Ubiquitous Cloud-Based Context-Aware Recommendation System for Mobile Social Networks[J].IEEE Transactions on Ser-vices Computing,2014,7(3):401-414.
[7]ZOU J,FEKRI F.On top-N recommendation using implicit user preference propagation over social networks[C]∥IEEE International Conference on Communications.IEEE,2014:3919-3924.
[8]李永凯,刘树波,杨召唤,等.机会网络中用户属性隐私安全的高效协作者资料匹配协议[J].通信学报,2015,36(12):163-171.
[9]梁俊杰,孙阳征.基于PH-Tree多属性索引树的朋友推荐算法[J].计算机科学,2015,42(4):156-159.
[10]LI F,WANG H,NIU B,et al.A practical group matching scheme for privacy-aware users in mobile social networks[C]∥Wireless Communications and NETWORKING Conference.IEEE,2016.
[11]LI M,NA R,QIAN Q,et al.SPFM:Scalable and Privacy-Preserving Friend Matching in Mobile Cloud[J].IEEE Internet of Things Journal,2017,4(2):583-591.
[12]GUO L,ZHANG C,FANG Y.A Trust-Based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks[J].IEEE Transactions on Dependable & Secure Computing,2015,12(4):413-427.
[13]HUANG S,ZHANG J,DAN S,et al.Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic Model[J].IEEE Transactions on Multimedia,2017,19(6):1314-1326.
[14]HINTON G.Learning distributed representations of concepts [C]∥Eighth Annual Conference of the Cognitive Science Society.1986:1-12.
[15]BLEI D M,NG A Y,JORDAN M I.Latent dirichlet allocation[J].J Machine Learning Research Archive,2003,3:993-1022.
[1] 吴子仪, 李邵梅, 姜梦函, 张建朋.
基于自注意力模型的本体对齐方法
Ontology Alignment Method Based on Self-attention
计算机科学, 2022, 49(9): 215-220. https://doi.org/10.11896/jsjkx.210700190
[2] 王剑, 彭雨琦, 赵宇斐, 杨健.
基于深度学习的社交网络舆情信息抽取方法综述
Survey of Social Network Public Opinion Information Extraction Based on Deep Learning
计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099
[3] 熊罗庚, 郑尚, 邹海涛, 于化龙, 高尚.
融合双向门控循环单元和注意力机制的软件自承认技术债识别方法
Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism
计算机科学, 2022, 49(7): 212-219. https://doi.org/10.11896/jsjkx.210500075
[4] 王毅, 李政浩, 陈星.
基于用户场景的Android 应用服务推荐方法
Recommendation of Android Application Services via User Scenarios
计算机科学, 2022, 49(6A): 267-271. https://doi.org/10.11896/jsjkx.210700123
[5] 魏鹏, 马玉亮, 袁野, 吴安彪.
用户行为驱动的时序影响力最大化问题研究
Study on Temporal Influence Maximization Driven by User Behavior
计算机科学, 2022, 49(6): 119-126. https://doi.org/10.11896/jsjkx.210700145
[6] 余皑欣, 冯秀芳, 孙静宇.
结合物品相似性的社交信任推荐算法
Social Trust Recommendation Algorithm Combining Item Similarity
计算机科学, 2022, 49(5): 144-151. https://doi.org/10.11896/jsjkx.210300217
[7] 畅雅雯, 杨波, 高玥琳, 黄靖云.
基于SEIR的微信公众号信息传播建模与分析
Modeling and Analysis of WeChat Official Account Information Dissemination Based on SEIR
计算机科学, 2022, 49(4): 56-66. https://doi.org/10.11896/jsjkx.210900169
[8] 左园林, 龚月姣, 陈伟能.
成本受限条件下的社交网络影响最大化方法
Budget-aware Influence Maximization in Social Networks
计算机科学, 2022, 49(4): 100-109. https://doi.org/10.11896/jsjkx.210300228
[9] 郭磊, 马廷淮.
基于好友亲密度的用户匹配
Friend Closeness Based User Matching
计算机科学, 2022, 49(3): 113-120. https://doi.org/10.11896/jsjkx.210200137
[10] 王剑, 王玉翠, 黄梦杰.
社交网络中的虚假信息:定义、检测及控制
False Information in Social Networks:Definition,Detection and Control
计算机科学, 2021, 48(8): 263-277. https://doi.org/10.11896/jsjkx.210300053
[11] 谭琪, 张凤荔, 王婷, 王瑞锦, 周世杰.
融入结构度中心性的社交网络用户影响力评估算法
Social Network User Influence Evaluation Algorithm Integrating Structure Centrality
计算机科学, 2021, 48(7): 124-129. https://doi.org/10.11896/jsjkx.200600096
[12] 张人之, 朱焱.
基于主动学习的社交网络恶意用户检测方法
Malicious User Detection Method for Social Network Based on Active Learning
计算机科学, 2021, 48(6): 332-337. https://doi.org/10.11896/jsjkx.200700151
[13] 鲍志强, 陈卫东.
基于最大后验估计的谣言源定位器
Rumor Source Detection in Social Networks via Maximum-a-Posteriori Estimation
计算机科学, 2021, 48(4): 243-248. https://doi.org/10.11896/jsjkx.200400053
[14] 张少杰, 鹿旭东, 郭伟, 王世鹏, 何伟.
供需匹配中的非诚信行为预防
Prevention of Dishonest Behavior in Supply-Demand Matching
计算机科学, 2021, 48(4): 303-308. https://doi.org/10.11896/jsjkx.200900090
[15] 袁得嵛, 陈世聪, 高见, 王小娟.
基于斯塔克尔伯格博弈的在线社交网络扭曲信息干预算法
Intervention Algorithm for Distorted Information in Online Social Networks Based on Stackelberg Game
计算机科学, 2021, 48(3): 313-319. https://doi.org/10.11896/jsjkx.200400079
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!