An efficient approach to user navigation pattern prediction

Abstract

User navigation pattern discovery involves learning the user's browsing behavior pattern while he is surfing on the web. It is a technique of discovering the hidden patterns from the web server log file. The web server log files contain an entire record of the user's browsing behavior. These log files can be further analyzed to extract user's navigation patterns. Using the discovered navigation patterns, prediction of which next page the user will be visiting can be made. Such information can be further used in recommendation, personalization, e-commerce websites or web caching. The approach proposed in this paper is based on a novel approach using K-harmonic means and Frequent Pattern Growth which identifies user's behavior patterns and predicts user's future request.