Web Page Recommendations Based on User Session Graph

Abstract

Web page recommender system provides users with recommendations to assist their navigation and increases the website usability and user satisfaction. In this chapter, Web page recommendation method is presented by constructing User Session Graph using user sessions from the navigation log. The node represents Web pages and weight on the edge is calculated by the number of times the Web pages present in the sessions. new page problem is solved by computing co-occurrence value between two terms present in titles. Web pages are recommended based on connected nodes in the graph and co-occurred terms. Experiments are conducted on user navigation data collected from Microsoft Website www.microsoft.com . The proposed method is compared with TermNetWP method and outperforms TermNetWP with higher precision and satisfaction values