Comparative Analysis of Machine Learning based Filtering Techniques using MovieLens dataset

The primary objective of the present work is two-fold. First, to compare the different filtering techniques namely BayesNet, Decision Table, Logistic, k-NN, JRip, LibSVM, Randomized Filtered Classifier, Random Forest, Random Tree and OneR on the basis of six evaluation metrics including Kappa Statistic, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), F- Measure, ROC Area and Accuracy. Second, to improve the accuracy with the resampling mechanism. To determine the most appropriate algorithm, we have compared them using WEKA tool on a large sample of MovieLens dataset. The performance of ten different training algorithms has been analyzed and presented in this paper. It is found that random forest has received better results in comparison with other techniques with 99.39% accuracy. In addition to this, we have also incorporated the discussion of on-going researches and related works carried in the domain of recommendation systems. The present research shall help the researchers in exploring the best classification techniques for improving accuracy.