LAUSR

Online shopping is becoming more and more common in our daily lives. Understanding users’ interests and behavior is essential to adapt e-commerce websites to customers’ requirements. The information about users’ behavior is stored in the Web server logs. The analysis of such information has focused on applying data mining techniques, where a rather static characterization is used to model users’ behavior, and the sequence of the actions performed by them is not usually considered. Therefore, incorporating a view of the process followed by users during a session can be of great interest to identify more complex behavioral patterns. To address this issue, this paper proposes a linear-temporal logic model checking approach for the analysis of structured e-commerce Web logs. By defining a common way of mapping log records according to the e-commerce structure, Web logs can be easily converted into event logs where the behavior of users is captured. Then, different predefined queries can be performed to identify different behavioral patterns that consider the different actions performed by a user during a session. Finally, the usefulness of the proposed approach has been studied by applying it to a real case study of a Spanish e-commerce website. The results have identified interesting findings that have made possible to propose some improvements in the website design with the aim of increasing its efficiency.