Optimizing selection using Pearson correlation coefficient: A case study in movie industry Available to Purchase

Selecting products available in the market is a very tedious process, thus a need to employed computing techniques to recommend something/products for the user is very important. The study focuses in developing a system that recommend movies with the used of three algorithms: content-based filtering, collaborative filtering and Pearson correlation coefficient. The system is a web-based platform that recommends movie titles based on attributes such as online queries, item description, user borrowing and movie historical data and user preferences. The implementation is successful and the combination of the implemented algorithms is not limited to providing movie recommendations alone, but can also be applied on different platforms e-learning, eCommerce, social media, news article websites, product selection and others.