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README.md

3bij3 - A framework for testing recommender systems and their effects

How to use it

(instructions work for MacOS and Linux systems, not tested on Windows yet)

It is recommended to use this app in a virtual environment.

  • Creating a virtual environment: python3 -m venv venv
  • activating a virtual environment: source venv/bin/activate

To use the app, you need all the packages listed in the requirements (you can install them with pip3 install -r requirements.txt).

Note: You might run into some issues regarding the mysqlclient package. On MacOS you can either use this solution if you use a brew installation; otherwise you first need to install MySQL and then pip-install mysql-connector-python. On Linux systems, this solution might help you

The way it is set up now you need an Elasticsearch database (infos on how to install this are here under point 3) for storing the news content you want to show to the user and an MySQL or SQLite database for storing the results.

Before running the app (you can also add this to your profile to not run it every time):

export FLASK_APP=3bij3.py

Furthermore, you should make an .env file to pass important information regarding passwords (for databases and email server) to the config.py file - or directly adapt the config.py file. Information on this as well as on how to connect to the MySQL database can be found here.

Note: In the link given above, you might need to change the line

create user 'microblog'@'localhost' identified by '<db-password>';

into

create user 'microblog'@'localhost' identified with mysql_native_password by '<db-password>';

In addition, you also need to initialize, migrate and upgrade the database. This can be done by running:

flask db init
flask db migrate
flask db upgrade

During development, you can start the app by typing "flask run", it then can be found in the browser under localhost:5000

To customize the application for your own needs, you can find all parameters that you can change in the vars.py file.

In the folder 'extras' are some scripts that can be used to build a SoftCosine similarity matrix (required for one of the recommenders), and to scrape, process, and annotate data. More info on how to use those follows soon. For this particular code, it is necessary to also use INCA. However, these files are only examples of how content can be retrieved and annotated for the application. You can also just have your own (annotated) data in an elasticsearch database without using these files.

More detailed instructions on how a flask app is built can be found in a highly recommended tutorial by Miguel Grinberg which was used to build this application. Here you can also find further information on how to put the application to production.

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Developing a news app with different recommender systems

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