Abstract
In order to fulfill the needs and preferences of today’s web users, adaptive Web applications have been proposed. Existing adaptation approaches usually adapt the content of pages according to the user interest. However, the adaptation of the interface structure to meet user needs and preferences is still incipient. In addition, building adaptive Web applications requires a lot of effort from developers. In this paper, we propose an approach to support the development of adaptive Web applications, analyzing the user behavior during navigation, and exploring the mining of client logs. In our approach, called RUM (Real-time Usage Mining), user actions are collected in the application’s interface and processed synchronously. Thus, we are able detect behavioral patterns for the current application user, while she is browsing the application. Facilitating its deployment, RUM provides a toolkit which allows the application to consume information about the user behavior. By using this toolkit, developers are able to code adaptations that are automatically triggered in response to the data provided by the toolkit. Experiments were conducted on different websites to demonstrate the efficiency of the approach to support interface adaptations that improve the user experience.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Velasquez, J.D., Palade, V.: Adaptive Web Sites: A Knowledge Extraction from Web Data Approach, vol. 170. IOS Press (2008)
Vasconcelos, L.G., Baldochi, Jr., L.A.: Towards an automatic evaluation of web applications. In: SAC 2012: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 709–716. ACM, New York (2012)
Goncalves, L.F., Vasconcelos, L.G., Munson, E.V., Baldochi, L.A.: Supporting adaptation of web applications to the mobile environment with automated usability evaluation. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, SAC 2016, pp. 787–794. ACM, New York (2016)
Vasconcelos, L.G., Santos, R.D.C., Baldochi, L.A.: Exploiting client logs to support the construction of adaptive e-commerce applications. In: Proceedings of the 13th International Conference on e-Business Engineering, ICEBE 2016, Macau, China, pp. 164–169 (2016)
Serdyukov, P.: Analyzing behavioral data for improving search experience. In: Proceedings of the 23rd International Conference on World Wide Web. WWW 2014 Companion, Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, pp. 607–608 (2014)
Khoury, R., Dawborn, T., Huang, W.: Visualising web browsing data for user behaviour analysis. In: Proceedings of the 23rd Australian Computer-Human Interaction Conference, OzCHI 2011, pp. 177–180. ACM, New York (2011)
Thomas, P.: Using interaction data to explain difficulty navigating online. ACM Trans. Web 8(4), 24:1–24:41 (2014)
Kuo, Y.H., Chen, J.N., Jeng, Y.L., Huang, Y.M.: Real-time learning behavior mining for e-learning. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 653–656, September 2005
Peska, L., Eckhardt, A., Vojtas, P.: Upcomp - a PHP component for recommendation based on user behaviour. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, WI-IAT 2011, vol. 03, pp. 306–309. IEEE Computer Society, Washington, DC (2011)
Apaolaza, A., Harper, S., Jay, C.: Understanding users in the wild. In: Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility, W4A 2013, pp. 13:1–13:4. ACM, New York (2013)
Abbar, S., Amer-Yahia, S., Indyk, P., Mahabadi, S.: Real-time recommendation of diverse related articles. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, pp. 1–12. ACM, New York (2013)
Mobasher, B., Cooley, R., Srivastava, J.: Creating adaptive web sites through usage-based clustering of URLs. In: Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX 1999), pp. 19–25 (1999)
Khonsha, S., Sadreddini, M.: New hybrid web personalization framework. In: 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 86–92, May 2011
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
de Vasconcelos, L.G., Baldochi, L.A., dos Santos, R.D.C. (2018). RUM: An Approach to Support Web Applications Adaptation During User Browsing. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-95165-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95164-5
Online ISBN: 978-3-319-95165-2
eBook Packages: Computer ScienceComputer Science (R0)