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
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
ACM SIGMOD Record
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
Research issues in data stream association rule mining
ACM SIGMOD Record
Time based patterns in mobile-internet surfing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 2006 ACM symposium on Applied computing
Regularity Analysis Using Time Slot Counting in the Mobile Clickstream
DEXA '06 Proceedings of the 17th International Conference on Database and Expert Systems Applications
An exploratory analysis on user behavior regularity in the mobile internet
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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The mobile Internet is characterized by "Easy-come and easy-go" characteristics, which causes challenges for many content providers. Enclosing end users and increasing mind share of each service are crucial for service adoption. The 24hour clickstream provides a rich opportunity to understand user's behaviors. It also raises the challenge of coping with a large amount of mobile web log data. The author examines a multi-day algorithm for user monthly-scale revisiting behavior classification for mobile video users. This was used in legacy text-oriented service in the past, however, the coverage of mobile video service users is still to be covered. In the case study section, the author shows the case studies in commercial mobile web sites and presents that the recall rate of the following month revisit prediction is approximately 80 %. The restriction of stream mining gives a small gap to the recall rates in literature, but the method has the advantage of small working memory to perform the given task of identifying the high revisit ratio users.