• KASIMALLA BHAVANI
• Dr. K.VENKATA RAO

Machine learning is a scientific study of algorithms which is used to learn from data without any human interaction. Recommendation systems or Recommender Systems come under Machine learning method which has become one of the essential systems in now a days e-commerce websites. Many techniques are proposed to efficiently capture the opinion of the users and to provide recommendations accurately. Recommendation System that seeks to predict the preference (rating) a user would give to an item. They are primarily used in commercial application. Collaborative filtering is one such successful method to provide recommendation to the users. In this paper, we demonstrate Collaborative filtering methods are classified as memory-based and model-based. Memory based algorithms: User based collaborative filtering (UBCF) and Item based collaborative filtering (IBCF). Model based algorithms: Kmeans and Random Forest Classification. Random forest predicts recommendations based on users preferences while targeting users interest and current trends. We evaluated the result with the help of the well-known MovieLens dataset show that random forest approach is more reliable than other algorithms in terms of RMSE value.

Recommendation System, Machine learning, Collaborative Filtering, Random Forest, RMSE

" An Efficient Recommendation System using Random Forest in Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.590-595, June 2019, Available :http://www.jetir.org/papers/JETIR1907B10.pdf

" An Efficient Recommendation System using Random Forest in Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp590-595, June 2019, Available at : http://www.jetir.org/papers/JETIR1907B10.pdf