微博网络用户的活跃性判定方法

推荐系统的冷启动问题是近期的研究热点,而用户的活跃性判定是冷启动问题的基础。已有方法在判定用户的活跃性时,单纯地考虑了用户发表信息量,对社交媒体的社交关系及行为等特征利用不够。该文面向微博网络,提出了系统的用户活跃性判定方法,创新性主要体现在: (1)提出了微博网络影响用户活跃性的四类指标,包括用户背景、社交关系、发表内容质量及社交行为,避免了仅仅使用用户发表信息数量判定用户是否活跃的粗糙方式;(2)提出了用户活跃性判定流程,提出了基于四类指标的用户与用户集的差异度计算模型。以新浪微博为例,选取了学术研究、企业管理、教育、文化、军事五个领域的900个用户作为测试集,使用准确率P、召回率R及F值为评价指标,进行了实验分析和比较。结果显示,该文所提用户活跃性判定方法的准确率P、召回率R、F值比传统的判定方法分别提高了21%、13%和16%,将该文所提方法用于用户推荐,得到的P、R和F值比最新的方法分别提高了5%、2%和3%,验证了所提方法的有效性。

User Activeness Determination in Microblog

To determining the user activeness,the existing methods mainly centered on the amount of information users posted,without proper utilizing the users- social relationship and behavior on microblog. This paper proposes a systematic method of determining the user activeness on microblog. In this method,four indexes are introduced to determinate users- activeness on microblog,including users- profile,social relationship,information quality and social behavior. And we also present the flow of determining the user activeness,and computation model for the diversity between a user and the whole user set. From Sina microblog,we select 900 users as the test set from the domain of academic research,business management,education,culture and military. Precision,Recall and F-value were used as evaluation index for experimental analysis and comparison among methods. The results show that our method improves the precision,recall and F-value of the user activeness determination by 21%,13% and 16%,respectively. Applying the proposed method to user recommendation,the precision,recall and F-value are increased by 5%,2% and 3%,respectively.