探索移动数据中的社交模式

P. Kasarapu, M. Saravanan, G. Prasad
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引用次数: 3

摘要

为了与其他电信供应商竞争,了解客户的行为并预测他们的需求是很重要的。为了实现这一点,需要根据客户的移动使用行为,将客户的详细信息挖掘成社交模式(细分),并针对合适的细分市场进行广告投放。在我们的方法中,客户的使用数据与其浏览行为相关联,用于形成被认为是重要补充的细分。通过分析用户在某一领域的使用情况,运营商可以深入到子领域层面的兴趣,并针对他们提供特定的定制服务。这可以通过使用Gibbs采样算法和K-Means聚类对其访问的网页及其使用和花费数据的描述进行潜在语义分析来完成。传统的方法是使用基于链接的方法形成网络社区。我们基于识别社会群体的方法可以为移动运营商提供另一种方法。特定集群内的使用率和客户对特定领域的兴趣可以帮助确定他们在特定领域的消费意愿程度。我们的方法通过使电信供应商能够针对特定的消费者群体,比传统方法产生更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring Social Patterns in Mobile Data
To compete with other telecom providers, it is important to understand the behavior of the customers and predict their needs. In order to realize this, it is required to explore the customers details based on their mobile usage behavior into social patterns (segments) and target the suitable segments for advertising. In our approach, the usage data of the customers in association with their browsing behavior is used to form the segments considered to be an important addition. From the analysis of their usage rates with respect to a certain domain, the operator can drill down to the sub domain level interests and target them with specific customized services. This can be done by performing latent semantic analysis using Gibbs sampling algorithm and K-Means clustering on the description of their accessed web pages with their usage and spend data. The traditional method involves forming web communities using link based approach. Our method based on identifying social communities could produce an alternative approach for the mobile operators. The usage rates within a certain cluster, and the customers' interest towards a specific domain can help to determine their extent of willingness to spend in specific areas. Our approach produces better results than the traditional methods by enabling the telecom providers to target a specific group of consumers.
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