- Contributors:
- Sizhe Chen
- Yandi Jin
- Miaoran Li
- Shuhan Xia
- Jing Xu
The movie industry has witnessed its prosperity for the last two decades.There are new movies produced everyday and we now have more film choices than ever before. However, it also becomes hard to tell if one movie is better than the other. So we want to create a movie recommender system to help our users pick the right high-quality movie that’s in their favors.
Our system is designed for film lovers who have difficulty deciding which movie to watch. There is no technical prerequisite so that all potential users are able to navigate through the system.
Our dataset contains 5 main files:
movies_metadata.csv: Contains information for 45,000 movies. Features include posters, backdrops, budget, revenue, release dates, languages, production countries and companies etc.
keywords.csv: Contains the movie plot keywords.
credits.csv: Consists of cast and crew Information.
links.csv: Contains the TMDB and IMDB IDs of all the movies.
ratings.csv: Contains 27,000,000 ratings and 1,100,000 tag applications applied to 58,000 movies by 280,000 users.Use cases.
The objective of the user interaction: Get 10 movies he/she may like
Use case 1:
For Simple filtering:
User input: 5 filters: genre, year, country, director, actor
System output: Recommended movies based on movie popularity
Use case 2:
For Content based filtering:
User input: Movie title
System output: Recommended movies based on the similarity to the given movie
Use case 3:
For collaborative filtering:
User input: User id
System output: Recommended movies by user based filtering and item based filtering. For user-based recommendation, the system recommends movies to the user that similar users like.
(a). Data manager:
What it does: Based on user’s input of different movie attributes (e.g. director, release year, language, etc.), filter out movies from the whole movie dataset.
Inputs it requires: Pick movie attributes from Dropdown
Outputs it provides: A subset of movies satisfying user picked attributes
(b). Visualization manager:
What it does: Display information (poster, movie overview, average rating, etc.) of filtered or recommended movies in a user interface.
Inputs it requires: None
Outputs it provides:
(c). Prediction manager:
What it does: Predicting rating and popularity of a movie based on input information
Inputs it requires: Cast, country, crew, category, company, budget, runtime etc.
Outputs it provides: Predicted rating and popularity of a movie based on input information.