通过增强数据挖掘，精准分析用户网页行为以优化体验
Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data
- 基于CAWAL框架融合多源日志，构建增强型交互数据集
- 87.16%会话跨页浏览，贡献98.05%页面访问量
- 发现服务访问模式，助力大规模用户体验优化
理解用户网络行为对优化用户体验（UX）日益重要。本研究提出增强型网页使用挖掘（AWUM），通过增强CAWAL（应用日志与网络分析融合框架）提供的交互数据，提升网页使用挖掘能力并改善用户体验。一个月内采集超过120万条会话记录（约8.5GB数据），经处理生成增强数据集。AWUM分析会话结构、页面请求、服务交互及退出方式。结果显示，87.16%的会话涉及多个页面，贡献了总页面访问量的98.05%；40%用户访问多种服务，50%选择安全退出。关联规则挖掘揭示了高频服务访问模式，证明CAWAL在精度和效率上优于传统方法。AWUM为用户行为提供了全面洞察，具备大规模用户体验优化的强潜力。
原文摘要 · Abstract (English)
Understanding user behavior on the web is increasingly critical for optimizing user experience (UX). This study introduces Augmented Web Usage Mining (AWUM), a methodology designed to enhance web usage mining and improve UX by enriching the interaction data provided by CAWAL (Combined Application Log and Web Analytics), a framework for advanced web analytics. Over 1.2 million session records collected in one month (~8.5GB of data) were processed and transformed into enriched datasets. AWUM analyzes session structures, page requests, service interactions, and exit methods. Results show that 87.16% of sessions involved multiple pages, contributing 98.05% of total pageviews; 40% of users accessed various services and 50% opted for secure exits. Association rule mining revealed patterns of frequently accessed services, highlighting CAWAL's precision and efficiency over conventional methods. AWUM offers a comprehensive understanding of user behavior and strong potential for large-scale UX optimization.
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