A recommendation system is a type of information filtering system that predicts or suggests items that a user might be interested in. In data science, recommendation systems are often used in e-commerce, social networking, and media industries to help users find products, services, and content that they are likely to enjoy or find useful. The two main types of recommendation systems are content-based filtering and collaborative filtering.
- Content-based filtering: recommends items based on their similarity to items that a user has liked in the past.
- Collaborative filtering: recommends items based on the preferences of similar users. This approach assumes that users who have similar tastes in the past are likely to have similar tastes in the future.
In this project, we'll implement content-based filtering method using movies overview that in TMBD movie website. And also in order to movies posters and links we'll use TMBD API.
First create a new environment than you need to import the requirements that the application needs using the requirements.txt file.
pip install -r requirements.txt
After downloading the codes and opening on your IDE, you can run the application with the terminal python run.py command. As you can see in the screenshot, when you click on "http://127.0.0.1:5000" the application will open in your browser.
