Task 4 (User Focused)

1. [10 pts] Read the ratings file to return a user-to-movies dictionary that maps user ID to the associated movies and the corresponding ratings. Write a function named read_user_ratings for this, with the ratings file as the parameter.

   For example: { u1: [ (m1, r1), (m2, r2) ], u2: [ (m3, r3), (m8, r8) ] }

   where ui is user ID, mi is movie, ri is corresponding rating.

2. [12 pts] Write a function get_user_genre that takes as parameters a user id, the user-to-movies dictionary (as created in Task 4.1 above), and the movie-to-genre dictionary (as created in Task 1.2), and returns the top genre that the user likes based on the user's ratings. Here, the top genre for the user will be determined by taking the average rating of the movies genre-wise that the user has rated.

3. [12 pts] Recommend 3 most popular (highest average rating) movies from the user's top genre that the user has not yet rated. Write a function recommend_movies for this, that takes a parameters a user id, the user-to-movies dictionary (as created in Task 4.1 above), the movie-to-genre dictionary (as created in Task 1.2), and the movie-to-average rating dictionary (as created in Task 2.2). The function should return a dictionary of movie-to-average rating. (Return all if fewer than 3 movies make the cut.)