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首页> 外文会议>International conference on advanced data mining and applications >Community Based User Behavior Analysis on Daily Mobile Internet Usage
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Community Based User Behavior Analysis on Daily Mobile Internet Usage

机译：基于社区的用户日常移动互联网使用行为分析

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摘要

Laptops, handhelds and smart phones are becoming ubiquitous providing (almost) continuous Internet access and ever-increasing demand and load on supporting networks. Daily mobile user behavior analysis can facilitate personalized Web interactive systems and Internet services in the mobile environment. Though some research have already been done, there are still some problems need to be investigated. In this paper, we study the community based user behavior analysis on the daily Mobile Internet usage. What we focus on in this paper is to propose a framework which can calculate the proper number of the clusters in mobile user network. Given a mobile user Internet access dataset of one week which contains thousand of users, we firstly calculate the hourly traffic variation for the whole week. Then, we propose to use cluster coefficient and network community profile to confirm the presence of communities in mobile user network. Principal Component Analysis (PCA) is employed to capture the dominant behavioral patterns and uncover the several communities in the network. At last, we use communities/clusters to work out the various interests of the users on the timeline of the day.
机译：便携式计算机，手持设备和智能电话正变得无处不在，提供（几乎）连续的Internet访问，并且对支持网络的需求和负载不断增加。日常的移动用户行为分析可以在移动环境中促进个性化的Web交互系统和Internet服务。尽管已经进行了一些研究，但是仍然需要研究一些问题。在本文中，我们研究了基于社区的日常移动互联网使用情况下的用户行为分析。我们在本文中重点关注的是提出一个框架，该框架可以计算移动用户网络中适当数量的群集。假设一个移动用户的互联网访问数据集为一个星期，其中包含数千个用户，我们首先计算整周的每小时流量变化。然后，我们建议使用聚类系数和网络社区资料来确认移动用户网络中社区的存在。主成分分析（PCA）用于捕获主要的行为模式并发现网络中的多个社区。最后，我们使用社区/集群在一天的时间轴上确定用户的各种兴趣。

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