The details of the movies(title, genre, runtime, rating, poster, etc) are fetched using an API by TMDB and using the IMDB id of the movie in the API, We did web scraping to get the reviews given by the user in the IMDB site using beautifulsoup4 and performed sentiment analysis on those reviews.
Check out the live demo: https://ml-movie-recommendation-system.herokuapp.com/
Incase if you are facing any issues in live demo, press ctrl+F5.
We've developed a similar application called "The Movie Cinema" which supports all language movies. But the only thing that differs from this application is that I've used the TMDB's recommendation engine in "Movie-Recommender-System". The recommendation part developed by me and kishan in this application doesn't support for multi-language movies as it consumes 200% of RAM (even after deploying it to AWS, Heroku ) for generating Count Vectorizer matrix for all the 700,000+ movies in the TMDB.
- Clone or download this repository to your local machine.
- Install all the libraries mentioned in the requirements.txt file with the command
pip install -r requirements.txt - Get your API key from https://www.themoviedb.org/.
- Open your terminal/command prompt from your project directory and run the file
application.pyby executing the commandpython application.py. - Go to your browser and type
http://127.0.0.1:5000/in the address bar.
How Cosine Similarity works? Cosine similarity is a metric used to measure how similar the documents are irrespective of their size. Mathematically, it measures the cosine of the angle between two vectors projected in a multi-dimensional space. The cosine similarity is advantageous because even if the two similar documents are far apart by the Euclidean distance (due to the size of the document), chances are they may still be oriented closer together. The smaller the angle, higher the cosine similarity.
Sources of the datasets