Patterns of search: analyzing and modeling Web query refinement
UM '99 Proceedings of the seventh international conference on User modeling
Rhythm modeling, visualizations and applications
Proceedings of the 16th annual ACM symposium on User interface software and technology
Hourly analysis of a very large topically categorized web query log
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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
Mobile web surfing is the same as web surfing
Communications of the ACM - Self managed systems
Coordinate: probabilistic forecasting of presence and availability
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Analysis of online video search and sharing
Proceedings of the eighteenth conference on Hypertext and hypermedia
Markov model based mobile clickstream analysis with sub-day, day and week-scale transitions
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Command transition probability analysis on mobile internet command sequences
NBiS'07 Proceedings of the 1st international conference on Network-based information systems
Mobile video user revisit analysis based on multi-day visiting patterns
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
CDMA mobile internet user behavior analysis based on RP interface
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part I
Temporal rules for predicting user navigation in the mobile web
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
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In this paper we investigate environmental factors that can result in users having different preferences and behaviors at different times of the day. An analysis is carried out of a large sample of user data for Wireless Application Protocol (WAP) browsing to determine whether user surfing patterns vary depending on time. We examine traffic on an hourly and daily basis, and show that accesses to particular categories of pages vary relative to time. We also build Markov models, which are temporal; to predict user navigation, and illustrate those predictive models are more accurate and beneficial to mobile Internet users than traditional methods. This analysis provides insight into improving the effectiveness and efficiency of navigation prediction.