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数据分析与知识发现  2019, Vol. 3 Issue (10): 66-77     https://doi.org/10.11925/infotech.2096-3467.2019.0043
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于用户聚类与动态交互信任关系的好友推荐方法研究 *
高慧颖(),魏甜,刘嘉唯
北京理工大学管理与经济学院 北京 100081
Friend Recommendation Based on User Clustering and Dynamic Interaction Trust Relationship
Huiying Gao(),Tian Wei,Jiawei Liu
School of Economics and Management, Beijing Institute of Technology, Beijing 100081,China
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摘要 

【目的】利用用户信息和社交网络拓扑信息, 提出基于用户聚类与动态交互信任关系进行精准好友推荐的方法。【方法】基于用户信息进行特征向量建模, 改进K-Prototypes算法分类型变量的距离计算公式, 并使用改进的K-Prototypes算法将最有可能成为好友的用户预先聚为k个簇类, 然后在每一簇中基于拓扑社交网络信任关系对目标用户进行好友推荐。从全局信任关系和交互信任关系两个维度衡量用户之间的拓扑网络信任关系, 并创新性地引入三个动态信任调节因子对交互信任度进行调节。最后在各个簇中融合全局信任度和动态交互信任度计算动态综合信任度, 基于此为用户产生Top-N好友推荐列表。【结果】通过与传统的好友推荐方法FOAF和SNS+Content进行比对, 本文基于用户聚类与动态交互信任关系的好友推荐方法在准确性、召回率、F1-Measure指标上均高于传统方法。【局限】本文的信任衡量模型只涉及多对一和一对一之间的群体信任关系, 暂未考虑到一对多、多对多的群体信任关系。【结论】本文综合利用用户信息和社交网络拓扑结构信息, 深度挖掘用户间交互行为变化所反映的动态信任关系, 能为社交用户做出更有效的好友推荐。

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作者相关文章
高慧颖
魏甜
刘嘉唯
关键词 好友推荐用户聚类信任度量动态交互信任    
Abstract

[Objective] This study proposes a method for friend recommendation based on user information and social network topology. [Methods] Firstly, we built a feature vector model with user information. To improve the accuracy and interpretability of the clustering results, we modified the distance calculation formula for categorical variables in the K-prototypes algorithm, which helped us pre-cluster the potential friends. Secondly, we recommended friends for the target users in each cluster based on the trust relationship of topological social network, which was measured from the global and interactive perspectives, as well as adjusted with the dynamic trust factors. Finally, we calculated the dynamic comprehensive trust with the global trust degree and the dynamic interactive trust of each cluster. A Top-N friend recommendation list was generated for the target user. [Results] Compared with traditional friend recommendation methods, the proposed method has better precision, recall and F1 values. [Limitations] The proposed model only addressed the group trust as many-to-one and one-to-one relationship. [Conclusions] The new method based on user clustering and dynamic interaction trust relationship is an effective way for online friend recommendation.

Key wordsFriend Recommendation    User Clustering    Trust Metrics    Dynamic Interaction Trust Relationship
收稿日期: 2019-01-10      出版日期: 2019-11-25
ZTFLH:  TP391 G35  
基金资助:*本文系国家自然科学基金项目“社交媒体健康知识发现与个性化诊疗方法研究”的研究成果之一(71572013)
通讯作者: 高慧颖     E-mail: huiying@bit.edu.cn
引用本文:   
高慧颖,魏甜,刘嘉唯. 基于用户聚类与动态交互信任关系的好友推荐方法研究 *[J]. 数据分析与知识发现, 2019, 3(10): 66-77.
Huiying Gao,Tian Wei,Jiawei Liu. Friend Recommendation Based on User Clustering and Dynamic Interaction Trust Relationship. Data Analysis and Knowledge Discovery, 2019, 3(10): 66-77.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2019.0043      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I10/66
  基于用户聚类与动态交互信任关系的好友推荐方法研究框架
  用户特征向量矩阵
  用户交互时间节点示例
  部分样本数据示例
  不同k值下两种算法得到的AD值
  k取10-20时对应的AD值变化率
  调和参数$\eta $评价指标的影响
  三种方法准确率、召回率、F1-Measure对比
  时间耗费情况对比
[1] Adamic L A, Adar E . Friends and Neighbors on the Web[J]. Social Networks, 2003,25(3):211-230.
[2] Colace F, De Santo M, Greco L , et al. A Collaborative User-Centered Framework for Recommending Items in Online Social Networks[J]. Computers in Human Behavior, 2015,51:694-704.
[3] 许超逸, 李德玉, 王素格 . 基于博文及网络结构信息的好友推荐方法[J]. 计算机工程与应用, 2016,52(1):55-60.
[3] ( Xu Chaoyi, Li Deyu, Wang Suge . Friend Recommendation Method Based on Micro-blogs and Network Structural Information[J]. Computer Engineering and Applications, 2016,52(1):55-60.)
[4] Makrehchi M. Social Link Recommendation by Learning Hidden Topics [C]// Proceedings of the 5th ACM Conference on Recommender Systems. ACM, 2011: 189-196.
[5] 汤颖, 钟南江, 范菁 . 一种结合用户评分信息的改进好友推荐算法[J]. 计算机科学, 2016,43(9):111-115.
[5] ( Tang Ying, Zhong Nanjiang, Fan Jing . Improved Friends Recommendation Algorithm Combining with User Rating Information[J]. Computer Science, 2016,43(9):111-115.)
[6] 马宏伟, 张光卫, 李鹏 . 协同过滤推荐算法综述[J]. 小型微型计算机系统, 2009,30(7):1282-1288.
[6] ( Ma Hongwei, Zhang Guangwei, Li Peng . Survey of Collaborative Filtering Algorithms[J]. Journal of Chinese Computer Systems, 2009,30(7):1282-1288.)
[7] Sarwar B, Karypis G, Konstan J, et al. Application of Dimensionality Reduction in Recommender Systems—A Case Study [C]// Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2000.
[8] 张彦龙 . 结合社会网络分析的协同过滤算法改进研究[D]. 广州: 华南理工大学, 2014.
[8] ( Zhang Yanlong . Research on Improved Collaborative Filtering Algorithm with Combination of Social Network Analysis[D]. Guangzhou: South China University of Technology, 2014.)
[9] 何静, 潘善亮, 韩露 . 基于双边兴趣的社交网好友推荐方法研究[J]. 计算机工程与应用, 2015,51(6):108-113.
[9] ( He Jing, Pan Shanliang, Han Lu . Recommendation Algorithm of SNS Friends Based on Bilateral Interest[J]. Computer Engineering and Applications, 2015,51(6):108-113.)
[10] Massa P, Avesani P. Trust-Aware Collaborative Filtering for Recommender Systems [C]// Proceedings of the OTM Confederated International Conferences. 2004: 492-508.
[11] Beth T, Borcherding M, Klein B. Valuation of Trust in Open Networks [C]// Proceedings of the 3rd European Symposium on Research in Computer Security. 1994: 1-18.
[12] Chang E, Thomson P, Dillon T, et al. The Fuzzy and Dynamic Nature of Trust [C]// Proceedings of the 2005 International Conference on Trust, Privacy, and Security in Digital Business. 2005: 161-174.
[13] Marsh S P . Formalising Trust as a Computational Concept[D]. Stirling: University of Stirling, 1994.
[14] 蒋江涛 . 社交网络中基于地理位置特征的社团发现方法研究与实现[D]. 北京: 北京航空航天大学, 2014.
[14] ( Jiang Jiangtao . Reasearch and Implementation of Community Detection Based on Geographical Feature for Social Networks[D]. Beijing: Beihang University, 2014.)
[15] 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.
[15] ( Huang Liang, Du Yongping . The Method of Latent Friend Recommendation Based on the Trust Relations[J]. Journal of Shandong University: Natural Science, 2013,48(11):73-79.)
[16] 尹光宇 . 社交网络中用户间信任度量模型研究[D]. 合肥: 中国科学技术大学, 2013.
[16] ( Yin Guangyu . Researches on Measurement Model for Trust Between Users in Social Networks[D]. Hefei: University of Science and Technology of China, 2013.)
[17] 张婷婷 . 基于社区发现的好友推荐方法研究[D]. 沈阳: 辽宁大学, 2016.
[17] ( Zhang Tingting . Research on Friend Recommendation Based on Community Detection[D]. Shenyang: Liaoning University, 2016.)
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