Developing and Evaluating Machine Learning Models in Recommendation Systems

In the current digital transformation era and e-commerce, recommendation systems (RS) have become vital in optimizing profits and finding and reaching potential customers. Large companies (Facebook, Google, Amazon, etc.) possess huge amounts of data about customers and goods and it is important to exploit information to create customer suggestion systems. Suitable products will improve customer experience, save advertising costs, and sell more products. Therefore, it will make huge profits from marketing and e-commerce. This paper aims to build and optimize popular models for the problem of RS on the MovieLens dataset. Therefore, we test and compare the capabilities predictions of these types of models. In addition, this project uses PySpark technology to increase the amount of data aiming for execution on large datasets in practice to evaluate how the amount of data affects the ability to suggest. We also present the models and the construction installation steps environment and optimize their parameters. We assess each algorithm on the Movielens 100k and 1M. The results show that the model has MAE as 81.21% and 87.47% on two datasets Movielens 100k and 20M.