Feature Engineering for Recommender Systems

by Benedikt Schifferer (Nvidia), Chris Deotte (Nvidia) and Even Oldridge (Nvidia)

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 cuDF and NVTabular. 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.

The tutorial is designed as a combination of theoretical lectures and practical exercises for the participants. We provide the lectures as pre-recordings and recommend all participants to watch them in advance to the 1 hour live session. In the 2×1 hour live sessions, participants get access to NVIDIA’s Deep Learning Institute (DLI) infrastructure and are able to explore the tutorial hands-on and work on the exercises. If participants have questions, we will answer/support the exercises in the chat. In parallel, we will provide a shortened version of our tutorial for participants, who haven’t had the opportunity to watch the pre-recordings.


Session A on 15:3016:30
Session B on 2:303:30

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