PNP: Mining of profile navigational patterns

Abstract

Web usage mining is a key knowledge discovery research and as such has been well researched. So far, this research has focused mainly on databases containing access log data only. However, many real-world databases contain users profile data and current solutions for this situation are still insufficient. In this paper we have a large database containing of user profile information together with users web-pages navigational patterns. The user profile data includes quantitative attributes, such as salary or age, and categorical attributes, such as sex or marital status. We introduce the concept of profile navigation patterns, which discusses the problem of relating user profile information to navigation behavior. An example of such profile navigation pattern might be "20% of married people between age 25 and 30 have the similar navigational behavior <(ac)(c,b)(b,e)(e,a)(od)> ", where a, b, c, d, e are web pages in a web site. The navigation sequences may contain the generic traversal behavior, e.g. trend to backward moves, cycles etc. The objective of mining profile navigation patterns is to identify browser profile for web personalization. We present PNP, a new algorithm that discovers these profile navigation patterns. Scale-up experiments show that PNP scales linearly with the number of transactions.