Recommender system has emerged as an integral part of the online shopping sites as it promotes sales. It recommends intuitive products based on users preference which solves the issue of information overload. Recommender system constitutes information filtering mechanism which filters vast amount of data. Algorithms such as SVD, KNN, Softmax Regression has already been used in the past to form recommendations. In this paper we propose a system which uses clustering and random forest as multilevel strategies to predict recommendations based on users ratings while targeting users mind-set and current trends. The result has been evaluated with the help of RMSE (Root Mean Square Error). Feasible performance has been achieved.
A. Ajesh, Jayashree Nair, and Jijin, P. S., “A Random Forest Approach for Rating-based Recommender System”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016.