ENHANCEMENT OF ONLINE WEB RECOMMENDATION SYSTEM USING A HYBRID CLUSTERING AND PATTERN MATCHING APPROACH

The rise in amount of information over internet in last few years has caused the growing risk of information flooding which in turn has created the problem of accessing relevant data to the users. Also with the hike in number of websites and web pages, webmasters find it challenging to formulate the content in accordance with the user's need. The information demand of the online users can be figured out by evaluating user's web navigation behavior. Web Usage Mining (WUM) is used to extract knowledge from Web user's access logs by employing Data Mining Techniques. One of the applications of WUM is Recommendation system which is personalized information filtering technique used to either determine whether a certain user will approve a given item or to identify a list of items which can be of significant importance to the user. In this paper an architecture that integrates product information with user's access log data and then generates a set of recommendations for that particular user is presented. The implementation has recorded encouraging results in terms of precision, recall and F1 metrics.