Github Recommender System

We used implicit ratings and an auto-encoder with a modified cost function to make a GitHub Recommender System. First, we collect the data, construct the confidence and prediction matrices based on implicit rating schemes. Finally, we train an auto-encoder with a modified cost function and test the trained model using Recall metric.