[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.
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