.png){kind=icon}
Return
Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data
.png){kind=icon}
PRE
AI
DOI：10.1080/10447318.2025.2495839.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A detailed understanding of user behavior on the web is crucial for optimizing user experience (UX) through data-driven analysis. This study introduces Augmented Web Usage Mining (AWUM), an approach that enhances web usage mining by enriching interaction data collected through the CAWAL (Combined Application Log and Web Analytics) framework. Over 1.2 million session records gathered within one month were transformed into 8.5 GB of enriched data and analyzed using AWUM to investigate session structures, page requests, service interactions, and exit behaviors across user segments. Results revealed that 87.16% of sessions involved multiple page visits, accounting for 98.05% of total pageviews. Furthermore, 76.2% of users accessed multiple services, and 57.2% of sessions involved secure exits during sensitive transactions. Association rule mining identified frequent service usage patterns, demonstrating AWUM's superiority in precision and efficiency compared to traditional web usage mining methods, thereby supporting the development of more effective UX strategies.
Keywords:
Web usage mining (WUM)
augmented WUM
user experience (UX)
behavior analysis
enriched analytics data
Journal
I
IF:
4.9
Papers:
4.5K
Citations:
1.2W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveEfficient Fuzzy C-means Based Reduced Feature Set Association Rule Mining Approach for Predicting the User Behavioral Pattern in Web Usage Mining.png)
網際網路技術學刊
OAAI
網際網路技術學刊IF0
OAAIShareSaveShareSave
View more