基于用户聚类与动态交互信任关系的好友推荐方法研究

Friend Recommendation Based on User Clustering and Dynamic Interaction Trust Relationship

【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。

[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.