ABSTRACT

With the rising growth of Web users, Web-based organizations are keen to analyze the on-line browsing behavior of the users in their web site and learn (identify) their interest instantly in a session. The analysis of the user’s current interest based on the navigational behaviour may help the organizations to guide the users in their browsing activity and obtain relevant information in a shorter span of time. However, the resulting patterns obtained through data mining techniques did not perform well in predicting the future browsing patterns due to the low matching rate of the resulting rules and users’ browsing behavior. This paper focuses on recommender systems based on the user’s navigational patterns and provides suitable recommendations to cater to the current needs of the user. The experimental results performed on real usage data from a commercial web site show a significant improvement in the recommendation effectiveness of the proposed system.