Improved Item Based Collaboration Filtering using recommendation System

Abstract

Recommender systems apply data analysis techniques to the problem of helping users find the items they would like to purchase at E-Commerce sites by producing a predicted likeliness score or a list of top-N recommended items for a given user. We apply improved K-mean algorithms method on preprocessed data. Finally we proposed a method that can increase accuracy based on previous K-mean. Recommender system applies knowledge discovery techniques to the problem of making personalized recommendation for information. Products or services during a live interaction. This system especially the k-nearest neighbor collaborative filtering based once, these are producing high recommendations performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsely. In traditional collaborative filtering system the amount of work increases with the number of participants in the system Keywords: -data analysis techniques; K-mean algorithms; recommendation System