Recommender systems are an essential component of online platforms providing users with personalized experiences. Some recommendation scenarios such as social networks and news are extremely dynamic in nature with user interests changing over time and new items being continuously added due to breaking news and trending events.

Recommender Systems (RecSys) are the engine of the modern internet and the catalyst for human decisions. The goal of a recommender system is to generate relevant recommendations for users from a collection of items or services that might interest them. Building a recommendation system is challenging because it requires multiple stages (item retrieval, filtering, ranking, ordering) to work together seamlessly and efficiently during training and inference. The biggest challenges faced by new practitioners are the lack of understanding around what RecSys look like in the real world and the difficulty in transitioning from the simple Matrix Factorization (MF) to more complex deep learning architectures with multiple input features, neural components and prediction heads.

Industrial recommender systems are made up of complex pipelines requiring multiple steps including feature engineering and preprocessing, a retrieval model for candidate generation, filtering, a feature store query, a ranking model for scoring, and an ordering stage. These pipelines need to be carefully deployed as a set, requiring coordination during their development and deployment. Data scientists, ML engineers, and researchers might focus on different stages of recommender systems, however they share a common desire to reduce the time and effort searching for and combining boilerplate code coming from different sources or writing custom code from scratch to create their own RecSys pipelines.

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.

The selection of features and proper preparation of data for deep learning or machine learning models plays a significant role in the performance of recommender systems. To address this we propose a tutorial highlighting best practices and optimization techniques for feature engineering and preprocessing of recommender system datasets. The tutorial will explore feature engineering using pandas and Dask, and will also cover acceleration on the GPU using open source libraries like RAPIDS and NVTabular. Proposed length is 180min. We’ve designed the tutorial as a combination of a lecture covering the mathematical and theoretical background and an interactive session based on jupyter notebooks. Participants will practice the discussed features by writing their own implementation in Python. NVIDIA will host the tutorial on their infrastructure, providing dataset, jupyter notebooks and GPUs. Participants will be able to easily attend the tutorial via their web browsers, avoiding any complicated setup. Beginner to intermediate users are the target audience, which should have prior knowledge in python programming using libraries, such as pandas and NumPy. In addition, they should have a basic understanding of recommender systems, decision trees and feed forward neural networks.