GitHub Recommender System

We built an Amazon-style recommender for GitHub by mining implicit feedback signals, constructing confidence-weighted interaction matrices, and training an autoencoder with a custom loss tailored to sparse developer-repository interactions. The resulting system delivers a recall of 0.72, offering relevant open-source projects to contributors and encouraging community participation.