Tracking The User’s Behaviour in E- Commerce Website

Authors

  • Smriti Gupta Department of CSE, SJB Institute of Technology, India
  • Komal Kumari Department of CSE, SJB Institute of Technology, India
  • Latha A Department of CSE, SJB Institute of Technology, India

Keywords:

Data mining, e-commerce, web logs analysis, behavioural patterns, model checking

Abstract

Online shopping is becoming more and more common in our daily lives. Tracking user’s interests and behaviour is essential in order to fulfil customer’s requirements. The information about user’s behaviour is stored in the web server logs. Absorbing a view of the process followed by user’s during a session can be of great interest to identify the behavioural patterns. The analysis of such information has focused on applying data mining techniques. Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. It is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. To address this issue, in this work we proposes a linear temporal logic model checking method for the analysis of structured e-commerce web logs.

References

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[2] F. M. Facca and P. L. Lanzi, “Mining interesting knowledge from weblogs: a survey,” Data & Knowledge Engineering, vol. 53, no. 3, pp. 225–241, 2005.

[3] J. Couvreur, “On-the-fly verification of linear temporal logic,” in Proceedings of Formal Methods: World Congress on Formal Methods in the Development of Computing Systems, Toulouse (France), September, 1999, pp. 253–271.

[4] S.D. Bernhard, C.K. Leung, V.J.Reimer, and J.Westlake, “Clickstream prediction using sequential stream mining techniques with markov chains,” in Proceedings of the 20thInternational Database Engineering & Applications Symposium, ser. IDEAS ’16. New York, NY, USA: ACM, 2016, pp. 24–33.

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Published

2025-11-26

How to Cite

[1]
S. Gupta, K. Kumari, and L. A, “Tracking The User’s Behaviour in E- Commerce Website”, Int. J. Comp. Sci. Eng., vol. 7, no. 15, pp. 257–260, Nov. 2025.

Issue

Vol. 7 No. 15 (2019): IJCSE Special Issue May Edition

Section

Review Article

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