摘 要
随着通信技术的快速发展，通信网络已经渗透到人们生活的各个方面。在这样的背景下，理解和分析用户在网络中的行为模式，以及通过建模来预测和优化这些行为，对于提升网络服务质量、优化资源分配以及实现个性化服务具有重要意义。本文旨在对通信网络中的用户行为分析与建模进行深入研究。用户行为分析是理解用户在通信网络中的活动模式、需求偏好以及行为规律的关键步骤。这涉及到对用户在网络中的多种活动进行收集和分析，如登录时间、访问的网站、在线时长、流量使用等。通过分析这些数据，我们可以发现用户的兴趣点、使用习惯以及潜在需求，为网络服务的优化和个性化推荐提供依据。在用户行为分析的基础上，建模研究旨在通过数学和统计方法，将用户行为转化为可预测、可优化的模型。这些模型可以帮助我们更准确地理解用户行为背后的规律和趋势，预测用户未来的行为模式，并据此优化网络资源的分配和提供个性化的服务。
关键词：通信网络 用户行为分析 建模研究
Abstract
With the rapid development of communication technology, communication network has penetrated into every aspect of people's life. In this context, understanding and analyzing users' behavior patterns in the network, as well as forecasting and optimizing these behaviors through modeling, are of great significance for improving network service quality, optimizing resource allocation and realizing personalized service. The purpose of this paper is to deeply study the user behavior analysis and modeling in communication network. User behavior analysis is the key step to understand the user's activity pattern, demand preference and behavior law in the communication network. This involves collecting and analyzing multiple activities of users on the network, such as login time, websites visited, online time, traffic usage, etc. By analyzing these data, we can find out users' interest points, usage habits and potential needs, and provide basis for network service optimization and personalized recommendation. Based on the analysis of user behavior, modeling research aims to transform user behavior into predictable and optimizable models through mathematical and statistical methods. These models can help us more accurately understand the laws and trends behind user behavior, predict user behavior patterns in the future, and optimize the allocation of network resources and provide personalized services.
Keywords: Communication network User behavior analysis Modeling research
目 录
1 引言 1
2 用户行为分析基础 1
2.1 用户行为定义 1
2.2 用户行为数据来源 1
2.3 用户行为分析框架 1
3 用户行为数据预处理 2
3.1 数据清洗与预处理 2
3.2 数据集成与变换 2
3.3 特征提取与选择 3
4 用户行为分析与建模的应用 3
4.1 网络服务质量优化 3
4.2 用户服务个性化 4
4.3 安全与异常行为检测 4
5 结论 5
致 谢 6
参考文献 7
