Neural Learning to Rank using TensorFlow Ranking: A Hands-on Tutorial

Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang

Abstract: A number of open source packages harnessing the power of deep learning have emerged in recent years and are under active development, including TensorFlow, PyTorch and others. Supervised learning is one of the main use cases of deep learning packages. However, compared with the comprehensive support for classification or regression in open-source deep learning packages, there is a paucity of support for ranking problems. To address this gap, we developed TensorFlow Ranking: an open-source library for training large scale learning-to-rank models using deep learning in TensorFlow. The library is flexible and highly configurable: it provides an easy-to-use API to support different scoring mechanisms, loss functions, example weights, and evaluation metrics. In this tutorial, we will combine the theoretical and the practical aspects of TF-Ranking, and will cover how TF-Ranking can be effectively employed in a variety of learning-to-rank scenarios, and demonstrate how it can handle advanced losses, scoring functions and sparse textual features. Finally, we will provide a hands-on codelab using a learning-to-rank dataset which shows how to effective incorporate sparse features for ranking.