Improved Product Ranking for Recommendation System

Data mining is extraction or mining the knowledge from large amount of data. There are many data mining methods used for recommendation system. Which are classification, clustering and association rule discovery. Real life data needs to be pre-processed, it means data cleaning, filtering and transformation is performed in order to be used by machine learning techniques in the analysis step . Collaborative filtering recommender is to use the k -NN classifier and clustering techniques. It is highly dependent on an appropriate similarity or distance measure. Here Recommendation systems are the k -nearest neighbour itembased filtering , are achieving on the intelligent Web . Thus Recommendation system is very useful to find the valuable information according to user’s choice and from their past opinions. It means to rank the product according to the user’s preference. This research is the improving of the technique for product ranking in RS system. Product is in form of movie. Thus Data mining technology is used to help customers access their favorite information and products in e-commerce sites. KeywordsRecommendation, collaborative filtering, k-NN, Product ranking.