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Research on recommendation algorithm of social friendsbased on six-degree segmentation theory(PDF)


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Research on recommendation algorithm of social friendsbased on six-degree segmentation theory
Du Shuying12Ding Shifei1
1.School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China; 2.Department of Finance Information,Xuzhou Vocational College of Bioengineering,Xuzhou 221000,China
friends recommendation six degree segmentation algorithm friend ratings social network
In order to better broaden the user’s social circle and get more information resources through new friends,friends recommendation to become the most popular target of social networking. Based on the analysis of the existing friend recommendation algorithm,this paper puts forward the social friend recommendation algorithm based on the theory of six-degree segmentation. Specifically,first,the method is based on the idea of friend rating,according to the user’s historical behavior to rate users,similar rating users into a group,to reduce the time cost of large-scale friend recommendation; Secondly,considering the relationship of common concern between users and the time difference between users and friends,the similarity between users and friends is calculated. The performance of the Sina Weibo data set verification algorithm was used,and the final experiment proves that the accuracy and recall rate of the algorithm is improved.


[1] 陈杰,刘学军,李斌,等. 一种基于用户动态兴趣和社交网络的微博推荐方法[J]. 电子学报,2017,45(4):898-905.
Chen Jie,Liu Xuejun,Li Bin,et al. Personalized microblogging recommendation based on dynamic interests and social networking of users[J]. Acta Electronica Sinica,2017,45(4):898-905.
[2]陈克寒,韩盼盼,吴健. 基于用户聚类的异构社交网络推荐算法[J]. 计算机学报,2013,36(2):349-359.
Chen Kehan,Han Panpan,Wu Jian. User clustering based social network recommendation[J]. Chinese Journal of Computers,2013,36(2):349-359.
[3]刘玮,贺敏,王丽宏,等. 基于用户行为特征的微博转发预测研究[J]. 计算机学报,2016,39(10):1992-2006.
Liu Wei,He Min,Wang Lihong,et al. Research on microblog retweeting prediction based on user behavior features[J]. Chinese Journal of Computers,2016,39(10):1992-2006.
[4]廖祥文,郑候东,刘盛华,等. 基于用户行为的情感影响力和易感性学习[J]. 计算机学报,2017,40(4):955-969.
Liao Xiangwen,Zheng Houdong,Liu Shenghua,et al. Learning influences and susceptibilities for sentiments from users’behaviors[J]. Chinese Journal of Computers,2017,40(4):955-969.
[5]陈婷,朱青,周梦溪,等. 社交网络环境下基于信任的推荐算法[J]. 软件学报,2017,28(3):721-731.
Chen Ting,Zhu Qing,Zhou Mengxi,et al. Trust-based recommendation algorithm in social network[J]. Journal of Software,2017,28(3):721-731.
[6]霍峥,孟小峰,黄毅. PrivateCheckIn:一种移动社交网络中的轨迹隐私保护方法[J]. 计算机学报,2013,36(4):716-726.
Huo Zheng,Meng Xiaofeng,Huang Yi. PrivateCheckIn:Trajectory privacy-preserving for check-in services in MSNS[J]. Chinese Journal of Computers,2013,36(4):716-726.
[7]余学军. 六度分割理论成就SNS[J]. 信息网络,2008(11):37.
Yu Xuejun. Six-degree segmentation theory achievements SNS[J]. Information Network,2008(11):37.
[8]徐蕾,杨成,姜春晓,等. 协同过滤推荐系统中的用户博弈[J]. 计算机学报,2016,39(6):1176-1189.
Xu Lei,Yang Cheng,Jiang Chunxiao,et al. Game analysis of user participation in collaborative filtering systems[J]. Chinese Journal of Computers,2016,39(6):1176-1189.
[9]曹玖新,陈高君,吴江林,等. 基于多维特征分析的社交网络意见领袖挖掘[J]. 电子学报,2016,44(4):898-905.
Cao Jiuxin,Chen Gaojun,Wu Jianglin,et al. Multi-feature based opinion leader mining in social networks[J]. Acta Electronica Sinica,2016,44(4):898-905.
[10]符饶. 基于位置服务的潜在好友推荐方法[J]. 软件,2015,36(1):62-66.
Fu Rao. Potential friend recommended approach based on location services[J]. Software,2015,36(1):62-66.
[11]张晓军,李仕明,何铮. 社会关系网络密度对创新扩散的影响[J]. 系统工程,2009,27(1):92-97.
Zhang Xiaojun,Li Shiming,He Zheng. Impact of social network density on innovation diffusion[J]. Systems Engineering,2009,27(1):92-97.
[12]李颖,朱保平. 基于交互链路的相似用户推荐算法[J]. 南京理工大学学报,2018,42(2):183-188.
Li Ying,Zhu Baoping. Similar user recommendation algorithm based on interactive link[J]. Journal of Nanjing University of Science and Technology,2018,42(2):183-188.
[13]叶锡君,袁培森,郭小清,等. 基于用户兴趣和项目周期的协同过滤推荐算法[J]. 南京理工大学学报,2018,42(4):392-400.
Ye Xijun,Yuan Peisen,Guo Xiaoqing,et al. Collaborative filtering recommendation algorithm based on user interest and project cycle[J]. Journal of Nanjing University of Science and Technology,2018,42(4):392-400.


Last Update: 2019-09-30