[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.
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.
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.
( 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.
( 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.)
( 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.
( Jiang Jiangtao . Reasearch and Implementation of Community Detection Based on Geographical Feature for Social Networks[D]. Beijing: Beihang University, 2014.)
( 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.)