The implementation in Spark MLlib supports model-based collaborative filtering. In the model-based collaborative filtering technique, users and products are described by a small set of factors, also called LFs. In this section, we will see two complete examples of how it works toward recommending movies for new users.
Spark-based movie recommendation systems
Item-based collaborative filtering for movie similarity
Firstly, we read the ratings from a file. For this project, we can use the MovieLens 100k rating dataset from http://www.grouplens.org/node/73. The training set ratings are in a file called ua.base, while the movie item data is in u.item. On the other hand, ua.test contains the test set to evaluate our model. Since...