A Movie recommendation engine, recommends similar movies as per the user's need using content-based filtering.
Movie recommendation systems usually predict what movies a user will like based on the attributes present in previously liked movies. We built a recommendation engine for this system. A lot of factors can be considered while designing a movie recommendation system like the genre of the movie, actors present in it, or even the director of the movie. The engine recommends a movie based on all content-based attributes.
We have fitted the engine into a web application that can be used by any user. It shows some movies from our data-set. After selecting a movie, it recommends some similar moves from the data-set.
• Movie Data-set & data pre-processing• Selecting features of the dataset• Matrix of token counts of selected features• Similarly Matrix• Recommendation• Application with code sample• Evaluation
Common approach when designing recommendation systems is content-based filtering. Content-based filtering methods are based on a description of the item and a profile of the user’s preferences.These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user. Content-based recommendations treat recommendation as a user-specific classification problem and learn a classifier for the user’s likes and dislikes based on an item’s features
The recommendation engine implemented in this repository aims to providing movie recommendation based on the many features of the movies. It recommends similar movie of selected movie to a user. Recommendation systems are widely used in today’s era of internet searching for reliable and relevant information. While simple recommendation systems recommend users based on a few parameters, complex ones take many parameters into consideration. By implementing machine learning in recommendation systems.
-pip install -r requirements.txt
-python manage.py migrate
-python manage.py shell
-python manage.py runserver