Consider a user data base given below. Each row represents a distinct user and each column represents a movie. Each cell contains the rating of the movie from 1 to 5 as provided by the users, where 1 is the lowest rating and 5 is the highest rating. Movie Matrix Inception Titanic Amelie Love_Actually Terminator Elysium Avatar Users Sam 2 0 0 0 0 2 4 4 Tom 4 5 4 0 0 3 1 4 Louis 2 0 4 5 3 0 0 3 Max 3 4 5 5 3 3 2 0 Jia 4 4 0 0 0 3 1 0 0 Recommend three movies for Jia using collaborative filtering. Use cosine similarity to find two closest neighbors. The cosine similarity between two users Ui and Uj is computed using the following formula, where rim is the rating given the ith user for the mth movie, p is number of movies commonly rated by users Ui and Uj. For your convenience, C(Jia, Uj) are 0.4392, 0.7791, 0.1555, and 0.6110 for Sam, Tom, Louis, and Max respectively. You may use the average rating of the closest neighbors (treating no-rating as 0 rating)