Popcorn Saver is a movie recommendation web application developed as a project for 2IMW15: Web Information Retrieval and Data Mining, during the first quartile of the 2015/2016 academic year at TU Eindhoven. It was developed using the Movielens 100K dataset.
This code corresponds to the setup and maintenance server, which a exposes a REST API allowing developers to:
- Load movies from the original dataset to a MongoDB instance, used by the recommender component.
- Load movies from the original dataset to an Elasticsearch instance, used to implement boolean and parametric search from the client component.
- Scrape movie quotes from http://www.moviequotedb.com/.
- Get movie related tweets using the Twitter API.
- A running instance of Elasticsearch on its default port. Installation instructions can be found here: https://www.elastic.co/guide/en/elasticsearch/guide/current/_installing_elasticsearch.html
- A running instance of MongoDB on its default port. Installation instructions can be found here: https://docs.mongodb.org/manual/installation/
- The Movielens 100K CSV files, which can be downloaded from: http://grouplens.org/datasets/movielens/
- Python 2.7 and pip
- Clone the project.
$ git clone https://github.com/rparrapy/popcorn-saver-server.git && cd popcorn-saver-server- Create a static/csv folder and move the Movielens CSV files there.
- Create and activate a virtualenv (optional but highly recommended).
$ virtualenv . && source bin/activate- Install Python dependencies.
$ pip install -r requirements.txt- Run the web server.
$ python server.pyThe maintenance server should be running on port 5000.
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Load movies to MongoDB by sending a POST request to /movies with a parameter dest=MONGO with your favorite HTTP client.
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Load movies to Elasticsearch by sending a POST request to /movies with a parameter dest=ES with your favorite HTTP client.
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Load ratings to MongoDB by sending a POST request to /ratings with your favorite HTTP client.