Data Recommendation Engines

This 13-video course explores recommendation engines, systems which provide various users with items or products that they may be interested in by observing their previous purchasing, search, and behavior histories. They are used in many industries to help users find or explore products and content; for example, to find movies, news, insurance, and a myriad of other products and services. Learners will examine the three main types of recommendation systems: item-based, user-based or collaborative, and content-based. The course next examines how to collect data to be used for learning, training, and evaluation. You will learn how to use RStudio, an open-source IDE (integrated development environment) to import, filter, and massage data into data sets. Learners will create an R function that will give a score to an item based on other user ratings and similarity scores. You will learn to use R to create a function called compareUsers, to create an item-to-item similarity or content score. Finally, learn to validate and score by using the built-in R function RMSE (root mean square error).