A Novel Approach for Recommending Items based on Association Rule Mining

Abstract

Currently online shopping has become a trend. People prefer to go for online shopping. Rather than going out and shopping for themselves as it provides an easier and quicker way to purchase items of their choice with quick transactions. Recommendation systems are widely used for recommending products to the end users in their interested fields. In the existing system, most recommendations are given to the users based on the browsing history which may or may not be of user’s interest and also the quality of the recommended items may not be guaranteed. This paper aims to find the efficient approach using Data Mining concept called Association Rule Mining with content-based 
and collaborative filtering in order to recommend the only relevant information to the buyers. The items are recommended for the buyers based on the content of buyers past buying history and opinion of other users in order to find out the quality of the item.  Association Rule mining is used for extracting the useful information from the transaction dataset and produce efficient and effective recommendation based on buyer’s interest thus satisfying the buyer in better way. Similarly for music and videos the recommendation is based on the keywords set by the Business Entity using Feature-based recommendation system.