Today’s business scenarios have been changed with the advent of E-commerce. More & more people have taken to the internet for doing B2B transaction. Further many web have exhibited a variety of navigational interests by clicking through variety of sequences of web pages. Now during their navigation web users are leaving the record of their web activities. So this record can be a useful source of information for tracking the user’s behaviour / preference for a product. Now with the development of Recommender systems, the e-commerce websites are able to use that records are gauge the customer’s preference & are able to suggest a product to the user which the customer will find valuable among the available list of products. In this paper we are proposing a hybrid recommender system. The proposed system works in two phases. In the first phase, user opinions are collected in the form of user-item rating matrix & are clustered offline & then stored in a database for future recommendation. In the second phase the recommendations are generated online for the active user by choosing the clusters with good quality ratings

1. Marko Balabanovic and Yoav Shoham1997.Fab:Content-based collaborativerecommendation.Communicaiotns of the ACM,40(3):pp.66-72
2. Wang Xiao-gang and LI YUE “Web mining based on user access patterns for web personalization.IEEE
3. LIANG WE and Zhao Shu-Hai “A hybrid recommender system
4. Liang HE and Faqing WU “A time-context-based collaborative Filtering algorithm
5. Sneha Y.S “online recommendation system based on web usage mining and semantic web uing lcs algorithm,IEEE
6. Zied Zaier,Robert Godin and Luc Faucher “Evaluating Recommender Systems,IEEE
7. Xiaosheng YU Shan Sun,”Research on personalized recommendation system based on web mining.IEEE
8. Xia Min-jie and Zhang Jin-ge,”research on personalized recommendation system for e-commerce based on web log mining and user browsing behaviours.IEEE2010

Web personalized recommender system web page clustering