AS shown in Figure 1, I really planned to implement a systematic project using factorization machines it turns out to be time constraint. Therefore, decided to develop a movie recommendation using a collaborative filtering  approach. Collaborative filtering based methods are classified as:

• Memory-based, that is, a user-based algorithm
• Model-based collaborative filtering, that is, kernel-mapping

In the model-based collaborative filtering technique, users and products are described by a small set of factors, also called latent factors (LFs). The LFs are then used to predict the missing entries. The Alternating Least Squares (ALS) algorithm is used to learn these LFs. From a computational perspective, model-based collaborative filtering is commonly used in many companies such as Netflix for real-time movie recommendations.