Analyzing clickstreams using subsessions
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising
Data Mining and Knowledge Discovery
Unique Identifier Tracking Analysis: A Methodology to Capture Wireless Internet User Behaviors
ICOIN '01 Proceedings of the The 15th International Conference on Information Networking
Golden Path Analyzer: using divide-and-conquer to cluster Web clickstreams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A clickstream-based collaborative filtering personalization model: towards a better performance
Proceedings of the 6th annual ACM international workshop on Web information and data management
Predicting navigation patterns on the mobile-internet using time of the week
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Emerging research methods for understanding mobile technology use
OZCHI '05 Proceedings of the 17th Australia conference on Computer-Human Interaction: Citizens Online: Considerations for Today and the Future
Proceedings of the 2009 International Conference on Hybrid Information Technology
Mobile video user revisit analysis based on multi-day visiting patterns
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
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The ever-changing nature of the mobile Internet contributes to the difficulties encountered when experts try to identify the user behavior characteristics. Using thin channels with so-called 24-hour 365-day always on nature, it is crucial to understand regularity of user access in the mobile Internet. It is leveraged by the mobile Internet-specific features like user identifies provided by wireless carriers. The author attempts to identify the easy-gone mobile Internet users from regularity dimension using a long-term user log with user identifiers. The author proposes an interval probability comparison method to predict the user behavior in the next month. The experiment from the mobile clickstream data shows the positive effect of the proposed method.