Study of Machine Learning for Recommendation Systems

This study provides an overview of recommendation systems and machine learning and their types. It briefly outlines the types of machine learning, such as supervised, unsupervised, semi-supervised learning and reinforcement. It explores how to implement recommendation systems using three types of filtering techniques: collaborative filtering, content-based filtering, and hybrid filtering. The machine learning techniques explained are clustering, co-clustering, and matrix factorization methods, such as Single value decomposition (SVD) and Non-negative matrix factorization (NMF). It also discusses K-nearest neighbors (KNN), K-means clustering, Naive Bayes and Random Forest algorithms. The evaluation of these algorithms is performed on the basis of three metric parameters: F1 measurement, Root mean squared error (RMSE) and Mean absolute error (MAE). For the experimentation, this study uses the BookCrossing dataset and compares analysis based on metric parameters. Finally, it also graphically depicts the metric parameters and shows the best and the worst techniques to incorporate into the recommendation system. This study will assist researchers in understanding the summary of machine learning in recommendation systems. 

Keywords: F1-measure, Machine learning, Mean absolute error (MAE), Nearest k- neighbors (KNN), Non-negative matrix factorization (NMF), Recommendation system, Root mean squared error (RMSE), Singular value decomposition (SVD).