In the last couple of years, deep learning (DL) has become the main enabler for applications in many domains such as vision, NLP, audio, clickstream data etc. Recently, researchers started to successfully apply deep learning methods to graph datasets in domains like social networks, recommender systems, and biology, where data is inherently structured in a graphical way.
So how do Graph Neural Networks work? Why do we need them?
In machine learning tasks that involve graphical data, we usually want to describe each node in the graph in a way that allows us to feed it into some machine learning…
For the past year, my team and I have been working on a personalized user experience in the Taboola feed. We used Multi-Task Learning (MTL) to predict multiple Key Performance Indicators (KPIs) on the same set of input features, and implemented a Deep Learning (DL) model in TensorFlow to do so. Back when we started, MTL seemed way more complicated to us than it does now, so I wanted to share some of the lessons learned.
Algorithms Developer at Taboola, works on Machine Learning applications for Recommendation Systems.