MovieSearch is a content-specific search engine with the aim to retrieve movie information given the contents of the user's query. The search engine relies on the OkapiBM25 algorithm and takes into consideration the overview, the title, the name of the director, of the actors and of the production companies of each movie. The backend has been developed with the framework Django while the front-end extensively relies on Bootstrap 4. Movies data, reviews and a reverse index to speed up the research are stored in a MongoDB database. It has also been implemented a movies recommendation system to recommend similar movies exploiting a nearest neighbors machine learning algorithm. Moreover, before uploading, movies reviews have been classified into two categories, positive and negatives, exploiting a pretrained Bert model fine tuned on the Imdb reviews dataset.
- Download movies posters dataset from https://www.kaggle.com/neha1703/movie-genre-from-its-poster/discussion/35485 and put all images into the folder
moviesearch/static/posters. - Download movies reviews from http://ai.stanford.edu/~amaas/data/sentiment/ and put the folder
aclImdbin the pathmoviesearch\utils. - The website relies on MongoDB, make sure it is running on your machine before going on. Otherwise you can download and install it from https://www.mongodb.com/.
- Install all dependencies with the command:
pip install requirements.txt.
- To clean, preprocess and upload movies data run the command
python moviesearch/utils/upload_movies.py. Note that the execution of this process includes computing movies recommendations for about 5000 movies with with nearest neightbors, thus it may take a few hours depending on your hardware. - To clean, preprocess and upload movies reviews run the command
python moviesearch/utils/upload_reviews.py. It will upload around 50k movies reviews.
- Finally, you can run your server with the command
python manage.py runserver. - You will find the website at the location
http://127.0.0.1:8000/.
- https://www.kaggle.com/neha1703/movie-genre-from-its-poster/discussion/35485
- http://ai.stanford.edu/~amaas/data/sentiment/
- https://www.mongodb.com/
- https://data-flair.training/blogs/django-tutorial/
- https://www.kaggle.com/rounakbanik/the-movies-dataset
- https://www.kaggle.com/fabiendaniel/film-recommendation-engine
- https://www.kaggle.com/tmdb/tmdb-movie-metadata