An Embedding Learning Framework for Numerical Features in CTR Prediction
In this post we're going to review a paper written by our Chinese friends and for which I couldn't find an implementation out there (at least using PyTorch and TF; note that you can find an implementation in MindSpore but I felt quite discouraged to go through their code as it uses a lot of wrappers). This paper is perfect for beginner to intermediate level Machine Learning coders to get their hands on implementing NN papers. The story behind me reading this paper is pretty straightforward: I was making some research about building an expressive deep learning model for a kind of CTR (click-through rate, meaning number of clicks divided by number of impressions) prediction model at work, and it turns out that DeepFM (Deep Factorisation Machines) was state-of-the-art (at least at the time of reading). Building a CTR model usually means you're working with some kind of search data, which usually comes flavoured with high-dimensional categorical features, with each feature...