End-to-End Session-Based Recommendation on GPU

Gabriel de Souza Pereira Moreira, Sara Rabhi, Ronay Ak, Benedikt Schifferer

Abstract: In recent years, several deep learning-based algorithms have been proposed for recommendation systems while its adoption in industry deployments have been steeply growing. In particular, NLP-inspired approaches have been successfully adapted for sequential and session-based recommendation problems, which are important for many domains like e-commerce, news and streaming media. In this regard, this hands-on tutorial will offer to the participants: (I) an introduction on the main concepts and algorithms for session-based recommendation, (II) how to build, train and evaluate a session-based recommendation model based on RNN and Transformer architectures, and (III) how to speed up with GPUs the entire RecSys pipeline which encompasses feature engineering, preprocessing, training, evaluation and inference using NVIDIA Merlin - an open source ecosystem for large-scale deep learning recommender systems.