ITEM BASED COLLABORATIVE FILTERING FOR RECOMMENDATION SYSTEM

Abstract

Recommendation system is used to recommend information to users as per their expectations and provide
personalized services through analyzing the behaviors. Buying trend in India shifting from time consuming shop visits to
highly flexible online shopping. There is increasing number of customers for online shopping and their likes and dislikes are
also different so it is very challenging to generate recommendation system. These are producing high quality
recommendations, performing many recommendations per second for millions of users and items. Previous collaborative
filtering systems the amount of work increases with the number of participants in the system. New recommender system
technologies are needed that can quickly produce high quality recommendations,even for very large-scale problems. To
address these issues we have explored item-based collaborative filtering techniques. We are proposing item based
collaborative filtering technique in which,Item-item collaborative filtering, or item-based, or item-to-item, is a form
of collaborative filtering for recommender systems based on the similarity between items calculated using people's ratings of
those items.