Description
You are required to design a prototype movie recommendation program in Python
according to the following guidelines.
You have the option to work individually or with a partner. But no groups over 2 people!
If you choose to work with a partner, you don’t have to tell us upfront who you will be
working with. We will post an announcement on Canvas in a few days that will detail
how to inform us about your partnership.
Make sure you abide by the DCS Acadmic Integrity Policy for Programming
Assignments. (Links to an external site.)
Write all the required functions described below in a file named hw1.py You may also
implement other helper functions as needed.
Submit ONLY your hw1.py file to Canvas.
If you are working with a partner, submit only ONE copy (either of you can submit, you
will both get the same score.)
Do not submit any other files, do not submit a zip file. We don’t need you to submit any
files you may have used to test your program. (Make sure to test your programs on files
other than the samples we have provided, to cover the various paths of logic in your
code.)
You are allowed up to 3 submissions (total over regular and late submissions), only the
last submission will be graded. Note: If you have a late submission, it will override any
submissions you may have made before the regular deadline, and you will be assessed
the 15 point penalty.
You can test your program by adding calls to various functions in the hw1.py file. You
may retain this test code when submitting, but we will ignore all of it. When we test your
submission, we will call the required functions individually on our own test data, and
points will be assigned to each function based on the correctness of the returned value.
There is no partial credit for code structure, etc. Credit is given only when correct values
are returned from your functions. However, each function will be tested on several
cases. So for instance, if a function runs correctly on 2 out of 3 test cases, you will get
full points for the 2 cases and zero for the third. (In this sense, there is partial credit for
each function.)
Data Input
• Ratings file: A text file that contains movie ratings. Each line has the name
(with year) of a movie, its rating (range 0-5 inclusive), and the id of the user
who rated the movie. A movie can have multiple ratings from different users.
A user can rate a particular movie only once. A user can however rate
multiple movies. Here’s a sample ratings file Download sample ratings
file
• Movies file: A text file that contains the genres of movies. Each line has a
genre, a movie id, and the name (with year) of the movie. To keep it simple,
each movie belongs to a single genre. Here’s a sample movies
file Download sample movies file
You may assume that input files will be correctly formatted, and data types will be as
expected. So you don’t need to write code to catch any formatting or data typing errors.
Task 1: Reading Data
1. [10 pts] Write a function read_ratings_data(f) that takes in a ratings file, and
returns a dictionary. The dictionary should have movie as key, and the
corresponding list of ratings 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 and
returns a dictionary. The dictionary should have a one-to-one mapping
between movie and genre.
For example { “Toy Story (1995)” : “Adventure”, “Golden Eye (1995)”
: “Action” }
Watch out for unwanted leading and trailing whitespaces in movie and genre, remove
them when encountered.
Task 2: Processing Data
1. [8 pts] Genre dictionary
Write a function create_genre_dict that takes as a parameter a movie-togenre 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 movies than n, it should all return all movies in
order of top average ratings.)
2. [8 pts] Threshold Rating
Write a function filter_movies that takes as parameters a dictionary of movieto-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. Genre categories will be from those in the movie:genre dictionary created
in Task 1.2. Your code should handle the case when there are fewer than n
movies in the data, as in Task 3.1 above.
4. [8 pts] Genre Rating
One important analysis for the 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. 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 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 movieto-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-toaverage rating. (Return all if fewer than 3 movies make the cut.)
