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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:

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

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