Abstract
As a websites’ structure grow it is paramount to accommodate the alignment of user needs and experience with the overall websites’ purposes. Toward this requirement, the proposed website navigation recommendation system suggests to users, pages that might be of her interest based on past successful navigation patterns of overall site’s usage. Most of existing recommendation systems adopts traditionally one of the web mining branches. We take a different stance, on web mining usage, and alternatively considered the real time enactment of web analytic tools supported analysis given their current maturity and affordances. On this basis we provide a model, its implementation and evaluation for navigation based recommendations generation and delivery. The developed prototype adopted a SaaS orientation to promote the underlying functionalities integration within any website. Preliminary evaluation’s results seem to favor the validation of the present contribution rational.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mehtaa, P., Parekh, B., Modi, K., Solanki, P.: Web personalization using web mining: concept and research issue. Int. J. Inf. Educ. Technol. 2(5), 510 (2012)
Fragkou, P.: Information extraction versus text segmentation for web content mining. Int. J. Softw. Eng. Knowl. Eng. 23(8), 1109–1137 (2013)
Dinucâ, C.E.: Web structure mining. Ann. Univ. Petrosani Econ. 11(4), 73–84 (2011)
Han, Q., Gao, X., Wu, W.: Study on web mining algorithm based on usage mining. In: 9th CAID/CD, pp. 1121–1124 (2008)
Ackermann, P., Velasco, C.A., Power, C.: Developing a semantic user and device modeling framework that supports UI adaptability of Web 2.0 applications for people with special needs. In: International Cross-Disciplinary Conference on Web Accessibility (2012)
Parra, D.: Beyond lists: studying the effect of different recommendation visualizations. In: Proceedings of Sixth ACM Conference on Recommender Systems, pp. 333–336 (2012)
Alves, R., Belo, O., Costa, F.: Mining clickstream-based data cubes, In: 11th Database Engineering and Applications Symposium, pp. 120–128 (2007)
Liu, B., Mobasher, B., Nasraoui, O.: Web data mining. In: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, pp. 449–483 (2007)
Suguna, R., Sharmila, D.: An efficient web recommendation system using collaborative filtering and pattern discovery algorithms. Int. J. Comput. Appl. 70(3), 37–44 (2013)
Hassan, M.T., Karim, A.: Impact of behavior clustering on web surfer behavior prediction. J. Inf. Sci. Eng. 27(6), 1855–1870 (2011)
Slimani, T., Lazzez, A.: Efficient analysis of pattern and association rule mining approaches. Int. J. Inf. Technol. Comput. Sci. 6(3), 70–81 (2014)
Abramson, M.: Learning temporal user profiles of web browsing behavior. Nav. Res. Lab. 1–9 (2014)
Nakatani, K., Chuang, T.: A web analytics tool selection method: an analytical hierarchy process approach. Internet Res. 21(2), 171–186 (2011)
Li, C.: When does web-based personalization really work? The distinction between actual personalization and perceived personalization. Comput. Hum. Behav. 54, 25–33 (2016)
Chiarandini, L.: Characterizing and modeling web sessions with applications. Universitat Pompeu Fabra (2014)
Pérez, J.E., Valencia, X., Arrue, M., Abascal, J.: Elaborating a web interface personalization process. In: ACM International Conference Proceeding Series, pp. 1–4, 7–9 September 2015
Berendt, B., Mobasher, B., Spiliopoulou, M.: Measuring the accuracy of sessionizers for web usage analysis. In: Proceedings of Workshop on Web Mining, 1st SIAM International Conference on Data Mining, pp. 7–14 (2001)
Halfaker, A., Keyes, O., Kluver, D., Nguyen, T., Shores, K., Uduwage, A.: User session identification based on strong regularities in inter-activity time. In: 24th International Conference on WWW, pp. 410–418 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sapateiro, C., Gomes, J. (2017). Leverage Web Analytics for Real Time Website Browsing Recommendations. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-319-56538-5_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-56538-5_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56537-8
Online ISBN: 978-3-319-56538-5
eBook Packages: EngineeringEngineering (R0)