Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Lee, J., Podlaseck, M., Schonberg, E., Hoch, R.: Visualization and analysis of clickstream data of online stores for understanding web merchandising. Data Mining and Knowledge Discovery 5(1-2), 59–84 (2005)
Andersen, J., Giversen, A., Jensen, A., Larsen, R., Pedersen, T., Skyt, J.: Analyzing clickstreams using subsessions. In: Proceedings of the third ACM international workshop on Data warehousing and OLAP, pp. 25–32 (2000)
Guenduez, S., Oezsu, M.: A web page prediction model based on click-stream tree representation of user behavior. In: ACM KDD 2003, pp. 535–540 (2003)
Ali, K., Ketchpel, S.: Golden path analyzer: using divide-and-conquer to cluster web clickstreams. In: ACM KDD 2003, pp. 257–276 (2003)
Kim, D.H., Atluri, V., Bieber, M., Adam, N., Yesha, Y.: Web personalization: A clickstream-based collaborative filtering personalization model: towards a better performance. In: ACM WIDM 2004, pp. 88–95 (2004)
Yamakami, T.: Unique identifier tracking analysis: A methodology to capture wireless internet user behaviors. In: ICOIN-15, Beppu, Japan, pp. 743–748. IEEE Computer Society, Los Alamitos (2001)
Yamakami, T.: A mobile clickstream time zone analysis: Implications for real-time mobile collaboration. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS, vol. 3214, pp. 855–861. Springer, Heidelberg (2004)
Halvey, M., Keane, M., Smyth, B.: Predicting navigation patterns on the mobile-internet using time of the week. In: WWW2005, pp. 958–959. ACM Press, New York (2005)
Hagen, P., Robertson, T., Kan, M., Sadler, K.: Emerging research methods for understanding mobile technology use. In: Proc. of 19th conf. of SIGCHI of Australia (OZCHI 2005), pp. 1–10 (2005)
Group, T.P.: Php hypertext processor (2003), Available at: http://www.php.net/
R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2005) ISBN 3-900051-07-0
Kamada, T.: Compact HTML for small information appliances. W3C Note, February 09, 1998 (1998), Available at: http://www.w3.org/TR/1998/NOTE-compactHTML-19980209
King, P., Hyland, T.: Handheld device markup language specification (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yamakami, T. (2006). An Exploratory Analysis on User Behavior Regularity in the Mobile Internet. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893011_18
Download citation
DOI: https://doi.org/10.1007/11893011_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46542-3
Online ISBN: 978-3-540-46544-7
eBook Packages: Computer ScienceComputer Science (R0)