C. Bharathipriya,B. Swathi,X. Francis Jency,



Recommender system,Collaborative filtering,decision making,Business-Consumer,


Recently customers are exposed to large variety of products and information on Internet, there is a necessity to filter, prioritize and personalize appropriate information to increase e-commerce demand. Using Recommender system, Business to Consumer (B2C) relationship can be benefitted and optimal, product selection is generated by solving voluminous data dynamically .In this work, a collaborative filtering is proposed to achieve top N recommendation about products to the consumers for purchase. In this work, the proposed recommender system focuses on obtaining similar group of customers using novel method. Personalized customer product recommendation is obtained by using classification and clustering algorithms. Good product evaluation is done using metrics like root mean square error (RMSE), mean square error (MSE). Recommender system has proved to enhance quality of decision making procedure and it gives a great impact on people’s decision making. This work gives a recommender system which increases the value of e-commerce websites and worthiness in encountering best products for customers. 


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