Recommendation System In Data Mining

Abstract

Now a day’s e-commerce widely uses recommendation system to recommend the customer by mining the historical data of purchases made by its customers. The recommendation system tries to analyze user and item tends do recommended relevant items to the customer. This helps them to increase their sales. The recommended system is either item based, user based or have combined approach. However, it does not automatically evaluate the accuracy of those recommendations. It has been observed that some of predicted recommendations have been irrelevant to the user. There is no mechanism to get the accuracy of those recommended items. There is also limitation with ranking of recommended items. A user clicks on the recommended items and clicks only on the most relevant item irrespective of its rank. This is a kind of inherent feedback given by the user by not clicking on irrelevant items. In this dissertation work improved relevant ranking and reduce the mean absolute error.