基于忠诚度的社交网络用户发现方法

针对社交网络中提高用户的高黏性问题，提出了一种基于用户忠诚度的用户发现的算法。该算法利用双重RFM模型对用户忠诚度进行计算，挖掘出忠诚度不同分类的用户。首先，通过双重RFM模型动态计算出用户在某一时间段的消费价值与行为价值，得到用户某一时间段的忠诚度；其次，根据用户的忠诚度，确定标度曲线，利用相似度计算找到典型的忠诚用户与不忠诚用户；最后，采用基于模块度的社区发现与独立级联传播模型，发现潜在的忠诚用户与不忠诚用户。在某社交网络的微博数据集上，实现了社会性网络服务（SNS）下用户忠诚度的量化表示，获得了基于用户忠诚度的用户发现结果。实验结果表明，所提算法能够有效挖掘出基于忠诚度的用户分类，可以为社交网站针对用户的个性化推荐及营销等，提供理论支持和实用方法。

User discovery based on loyalty in social networks

Aiming at improving the users' high viscosity in social networks, an algorithm based on user loyalty in social network system was proposed. In the proposed algorithm, double Recency Frequency Monetary (RFM) model was used for mining the different loyalty kinds of users. Firstly, according to the double RFM model, the users' consumption value and behavior value were calculated dynamically and the loyalty in a certain time was got. Secondly, the typical loyal users and disloyal users were found out by using the founded standard curve and similarity calculation. Lastly, the potential loyal and disloyal users were found out by using modularity-based community discovery and independent cascade propagation model. On some microblog datasets of a social network, the quantitative representation of user loyalty was confirmed in Social Network Service (SNS), thus the users could be distinguished based on users' loyalty. The experimental results show that the proposed algorithm can be used to effectively dig out different loyalty kinds of users, and can be applied to personalized recommendation, marketing, etc. in the social network system.