Description

Movie Recommendation System

Task 1: Reading Data

1. [10 pts] Write a function read_ratings_data(f) that takes in a ratings file name, and returns a
dictionary. (Note: the parameter is a file name string such as “myratings.txt”, NOT a file pointer.)

The dictionary should have movie as key, and the list of all ratings for it as value.
For example: movie_ratings_dict = { “The Lion King (2019)” : [6.0, 7.5, 5.1],
“Titanic (1997)”: [7] }

2. [10 pts] Write a function read_movie_genre(f) that takes in a movies file name and returns a
dictionary. The dictionary should have a one-to-one mapping from movie to genre.
For example { “Toy Story (1995)” : “Adventure”, “Golden Eye (1995)” : “Action”
}
Watch out for leading and trailing whitespaces in movie name and genre name, and remove them before
storing in the dictionary.

Task 2: Processing Data

1. [8 pts] Genre dictionary
Write a function create_genre_dict that takes as a parameter a movie-to-genre dictionary, of
the kind created in Task 1.2.

The function should return another dictionary in which a genre is
mapped to all the movies in that genre.
For example: { genre1: [ m1, m2, m3], genre2: [m6, m7] }

2. [8 pts] Average Rating
Write a function calculate_average_rating that takes as a parameter a ratings dictionary, of
the kind created in Task 1.1. It should return a dictionary where the movie is mapped to its
average rating computed from the ratings list.
For example: {“Spider-Man (2002)”: [3,2,4,5]} ==> {“Spider-Man (2002)”: 3.5}

Task 3: Recommendation

1. [10 pts] Popularity based
In services such as Netflix and Spotify, you often see recommendations with the heading “Popular
movies” or “Trending top 10”.

Write a function get_popular_movies that takes as parameters a dictionary of movie-to-average
rating ( as created in Task 2.2), and an integer n (default should be 10).

The function should return
a dictionary ( movie:average rating, same structure as input dictionary) of top n movies based on
the average ratings. If there are fewer than n movies, it should return all movies in ranked order of
average ratings from highest to lowest.

2. [8 pts] Threshold Rating
Write a function filter_movies that takes as parameters a dictionary of movie-to-average rating
(same as for the popularity based function above), and a threshold rating with default value of 3.

The function should filter movies based on the threshold rating, and return a dictionary with same
structure as the input.

For example, if the threshold rating is 3.5, the returned dictionary should
have only those movies from the input whose average rating is equal to or greater than 3.5.

3. [12 pts] Popularity + Genre based
In most recommendation systems, genre of the movie/song/book plays an important role. Often,
features like popularity, genre, artist are combined to present recommendations to a user.
Write a function get_popular_in_genre that, given a genre, a genre-to-movies dictionary (as
created in Task 2.1), a dictionary of movie:average rating (as created in Task 2.2), and an integer
n (default 5), returns the top n most popular movies in that genre based on the average ratings.

The return value should be a dictionary of movie-to-average rating of movies that make the cut. If
there are fewer than n movies, it should return all movies in ranked order of average ratings from
highest to lowest.

Genres will be from those in the movie:genre dictionary created in Task 1.2. The genre name will
exactly match one of the genres in the dictionary, so you do not need to do any upper or lower
case conversion.

4. [8 pts] Genre Rating
One important analysis for content platforms is to determine ratings by genre.
Write a function get_genre_rating that takes the same parameters as get_popular_in_genre
above, except for n, and returns the average rating of the movies in the given genre.

5. [12 pts] Genre Popularity
Write a function genre_popularity that takes as parameters a genre-to-movies dictionary (as
created in Task 2.1), a movie-to-average rating dictionary (as created in Task 2.2), and n (default
5), and returns the top-n rated genres as a dictionary of genre:average rating. If there are fewer
than n genres, it should return all genres in ranked order of average ratings from highest to
lowest. Hint: Use the above get_genre_rating function as a helper.

Task 4: User Focused

1. [10 pts] Read the ratings file to return a user-to-movies dictionary that maps user ID to a list of the
movies they rated, along with the rating they gave. 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. You can handle user ID as int or
string type, but make sure you consistently use it as the same type everywhere in your code.

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. If multiple genres have the same highest ratings for the user, return any one of genres
(arbitrarily) as the top genre.

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 as
parameters a user id, the user-to-movies dictionary (as created in Task 4.1 above), the movie-togenre 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. If fewer than 3
movies make the cut, then return all the movies that make the cut in ranked order of average
ratings from highest to lowest.