Movie recommendation algorithm

Final practice of the subject Data Structures that implements a movie recommendation algorithm using different structures like graphs, hashmaps and binary trees.

Motivation

This project uses the dataset provided from the Netflix's competition https://www.kaggle.com/datasets/netflix-inc/netflix-prize-data.

Project structure

• src/exceptions: exceptions raised in some cases by the program.

• src/io: methods implemented to load the information contained on the movies' dataset.

• src/models: models used for training and prediction of movie recommendations to a determined user.

• movie_titles.txt: contains the information about nearly 18000 movies. Is in the following format:

  MovieID,YearOfRelease,Title

MovieID do not correspond to actual Netflix movie ids or IMDB movie ids. YearOfRelease can range from 1890 to 2005 and may correspond to the release of corresponding DVD, not necessarily its theaterical release. Title is the Netflix movie title and may not correspond to titles used on other sites. Titles are in English.

• training_set/movie_users_20_50.txt: contains the information about diferent customers ratings and dates. Is in the following format:

  CustomerID,Rating,Date

MovieIDs range from 1 to 17770 sequentially. CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users. Ratings are on a five star (integral) scale from 1 to 5. Dates have the format YYYY-MM-DD.

How it works?

• Type "id" from any user/customer on movie_users_20_50.txt

• Then, the program will load the information using diferent data structures and then categorize users by their similarity to the user we want to recommend through different parameters.

• Finally the program will recommend between 0 and 3 movies to the user indicated.

Authors

• @mrobledo07 - Miguel Robledo Kusz