Paper Details

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

Shradha Basagre, Amruta Kamble, Supriya Zende, Gouri Tavate, Vaibhavi Kharge, “ITEM BASED COLLABORATIVE FILTERING FOR RECOMMENDATION SYSTEM”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 3, pp. 1198-1205, March 2018.