Building Recommender Systems with PyTorch

Dheevatsa Mudigere, Maxim Naumov, Joe Spisak, Geeta Chauhan, Narine Kokhlikyan, Amanpreet Singh, Vedanuj Goswami

Abstract: In this tutorial we show how to build deep learning recommendation systems and resolve the associated interpretability, integrity and privacy challenges. We start with an overview of the PyTorch framework, features that it offers and a brief review of the evolution of recommendation models. We delineate their typical components and build a proxy deep learning recommendation model (DLRM) in PyTorch. Then, we discuss how to interpret recommendation system results as well as how to address the corresponding integrity and quality challenges.