e-Loyalty: How to Keep Customers Coming Back to Your Website
e-Loyalty: How to Keep Customers Coming Back to Your Website
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
Research issues in data stream association rule mining
ACM SIGMOD Record
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
Online stickiness: its antecedents and effect on purchasing intention
Behaviour & Information Technology
A Weak-Tie Based Regularity Analysis of Mobile Clickstreams
AINAW '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops
A User-Perceived Freshness Clustering Method to Identify Three Subgroups in Mobile Internet Users
MUE '08 Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering
A Time Zone Index Method to Identify Time-Based Mobile User Interaction Patterns
ICMB '08 Proceedings of the 2008 7th International Conference on Mobile Business
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 proliferates and penetrates all aspects of daily life. The 4 billion mobile users will increase their impacts on data services. The author came to a sense that the intra-day mobile behaviors are classified into three user groups: always-on, night, and infrequent users. The author proposes a two-phase method to identify these three user groups. The author shows the case study result from 2001 commercial service logs with two different mobile services. The derived user groups are evaluated by the revisit ratio in the following month. The results are discussed to highlight the three-segment model in intra-day mobile user behaviors. They show the clear revisit ratio difference among the three user groups in the four services in observation. The two services in the two case studies are significantly different, however, they still exhibit consistent patterns in the three group classification.