基于好友亲密度的用户匹配

Friend Closeness Based User Matching

用户匹配的目的是检测来自不同社交网络的用户是否是同一个人。现有的研究主要集中在用户属性和网络嵌入上,而这些研究方法往往忽略了用户与好友间的亲密关系。因此,文中提出一种基于好友亲密度的用户匹配算法(FCUM)。该算法是一种半监督、端到端的跨社交网络用户匹配算法,其中注意力机制被用于量化用户与好友之间的亲密度。好友亲密度的量化能够提高FCUM的泛化能力。通过在单一目标函数中对用户个体相似性和亲密好友相似性进行联合优化,能充分利用用户个体相似性和亲密好友相似性。文中还设计了一种双向匹配策略,用于解决人工标记匹配用户代价较高的问题。在真实数据集上的实验表明,FCUM算法优于其他只考虑用户个体相似性的方法。在如今用户隐私保护限制愈发严格、难以获取用户其他完整属性信息的情形下,该算法具有实用和易于推广的特性。

The typical aim of user matching is to detect the same individuals cross different social networks.The existing efforts in this field usually focus on users’attributes and network embedding,but these methods often ignore the closeness between users and their friends.To this end,we present a friend closeness based user matching algorithm(FCUM).It is a semi-supervised and end-to-end cross social networks user matching algorithm.Attention mechanism is used to quantify the closeness between users and their friends.Quantification of close friends improves the generalization ability of the FCUM.We consider both individual similarity and their close friend similarity by jointly optimizing them in a single objective function.Due to the expensive costs of labeling new match users for training FCUM,we also design a bi-directional matching strategy.Experiments on real datasets illustrate that FCUM outperforms other state-of-the-art methods that only consider the individual similarity.In the situation that the privacy protection of users is becoming more and more strict and it is difficult to obtain other complete attribute information of users,the algorithm has the characteristics of practicality and easy promotion.