This project consists of designing a prototype recommender system that implements fairness through the mitigation of popularity bias. This recommender system provides both lesser-known and popular artists equal chance to be recommended to user(s). Python was the programming language used throuhgout the project and Streamlit was the app framework used to create a web app, which is used in Data Science and Machine Learning.
Side notes:
The login details can be found in login_data.csv. The Data_Synthesis.pynb was used to synthesize user listening behavior for 300 users.
The app folder contains all necessary files to run Streamlit app. The folder recommendations includes the recommendations file with the recommended songs. The file RS.py is the algorithm that is run every time the user presses the play button or chooses a preferred genre. The file activities.json stores all the activities the user makes within the Streamlit app The files app.py and template.py are used to run Streamlit.