【目的】利用用戶信息和社交網絡拓撲信息,提出基于用戶聚類與動態交互信任關系進行精準好友推薦的方法。【方法】基于用戶信息進行特征向量建模,改進K-Prototypes算法分類型變量的距離計算公式,并使用改進的K-Prototypes算法將最有可能成為好友的用戶預先聚為k個簇類,然后在每一簇中基于拓撲社交網絡信任關系對目標用戶進行好友推薦。從全局信任關系和交互信任關系兩個維度衡量用戶之間的拓撲網絡信任關系,并創新性地引入三個動態信任調節因子對交互信任度進行調節。最后在各個簇中融合全局信任度和動態交互信任度計算動態綜合信任度,基于此為用戶產生Top-N好友推薦列表。【結果】通過與傳統的好友推薦方法 FOAF和SNS+Content進行比對,本文基于用戶聚類與動態交互信任關系的好友推薦方法在準確性、召回率、F1-Measure指標上均高于傳統方法。【局限】本文的信任衡量模型只涉及多對一和一對一之間的群體信任關系,暫未考慮到一對多、多對多的群體信任關系。【結論】本文綜合利用用戶信息和社交網絡拓撲結構信息,深度挖掘用戶間交互行為變化所反映的動態信任關系,能為社交用戶做出更有效的好友推薦。

[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.