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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (10): 66-77    DOI: 10.11925/infotech.2096-3467.2019.0043
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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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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     
Received: 10 January 2019      Published: 25 November 2019
ZTFLH:  TP391 G35  
Corresponding Authors: Huiying Gao     E-mail: huiying@bit.edu.cn

Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0043     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I10/66

[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.)
[1] Wang Xiwei,Jia Ruonan,Wei Yanan,Zhang Liu. Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
[2] Xiong Huixiang,Ye Jiaxin,Jiang Wuxuan. Clustering Social Tags with Improved DBSCAN Algorithm[J]. 数据分析与知识发现, 2018, 2(12): 77-88.
[3] Wang Xiaoyun, Qian Lu, Huang Shiyou. Collaborative Filtering Recommendation Model Based on Rough User Clustering[J]. 现代图书情报技术, 2015, 31(1): 45-51.
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[5] Yu Yan, Qiu Guanghua, Chen Aiping. Friend Recommendation Algorithm Based on Mixed Graph in Online Social Networks[J]. 现代图书情报技术, 2011, (11): 54-59.
[6] Yan Duanwu,Luo Shengyang,Cheng Xiao . Toward User-Document Matrix Based User Clustering for Collaborative Recommendation[J]. 现代图书情报技术, 2007, 2(3): 25-28.
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