利用通信数据的移动用户行为分析

Analysis of Mobile User Behaviors with Telecommunication Data

【目的】了解移动用户的行为模式并建立用户模型。【方法】基于国内电信运营商随机抽取某市一万个移动用户一周的日志记录,包含4万余条通话记录和200余万条网络请求,每条请求包含对应的基站标号以及基站地理位置。从消费能力、通话量、网络请求量、位移量4个维度在这批数据中提取14种基本特征指标,并利用K-means方法聚类。【结果】将移动用户区分成规律通话型、随机上网型、居家节约型和随机高消费型4类用户模型。【局限】用户量与数据量有限,没有采用更复杂的机器学习算法构建用户模型。【结论】研究结果对移动应用个性化服务的改进具有重要的参考价值。

[Objective] This paper proposes a user model to understand mobile user behaviors. [Methods] Mobile user behaviors based on communication records from a Chinese telecom, including 10 thousand mobile users in a week with 40 thousand calls and 2 million network requests with locational information are analyzed. 14 fundamental indicators from the data are adopted based on four different categories, namely consumption level, call volume, network request, and amount of movement. [Results] Four user types, regular motion with large conversation, erratically motion with high network accessing, stay-in with economization, and erratically motion with high consumption, are finally deduced in this study by using K-means clustering method. [Limitations] Because of the limitation of user number and the quantity of data, complex machine learning methods are not used to create user model. [Conclusions] The results are valuable references to improve personalized services in mobile applications.