In episode six of the Grandmaster Series, learn how participating members of the Kaggle Grandmasters of NVIDIA (KGMON) used GPU-accelerated boosted trees and deep neural networks to build the winning recommender system in the ACM RecSys Challenge, hosted by Twitter. In this recommendation system challenge, the goal was to predict the probability for different types of engagement (Like, Reply, Retweet, and Retweet with Comment) of a target user for a set of tweets, based on heterogeneous input data. Watch the video to see their winning solution. Subscribe to our Youtube channel to be notified when we publish a new Grandmaster Series episode. If you have any questions during the video, you can submit them through chat. We will try to provide answers throughout and at the end of the episode. Video Chapters: 00:00 – Intro to Episode Six 03:20 – ACM RecSys Challenge Overview 19:10 – Neural Network Models 31:34 – Target Encoding & XGBoost Models 57:14 – Stacking Model Strategy 1:09:42 – Closing Remarks Additional Resources: 1. Paper: GPU Accelerated-Boosted Trees and Deep Neural Networks for Better Recommender Systems https://github.com/NVIDIA-Merlin/competitions/blob/main/RecSys2021_Challenge/GPU-Accelerated-Boosted-Trees-and-Deep-Neural-Networks-for-Better-Recommender-Systems.pdf 2. Using Neural Networks for Your Recommender System https://developer.nvidia.com/blog/using-neural-networks-for-your-recommender-system/ 3. How to Build a Winning Deep Learning Recommender System, E5 https://www.youtube.com/watch?v=bHuww-l_Sq0 4. How to Build a Winning Recommendation System, Part 1 https://developer.nvidia.com/blog/how-to-build-a-winning-recommendation-system-part-1/ 5. How to Build a Deep Learning Powered Recommender System, Part 2 https://developer.nvidia.com/blog/how-to-build-a-winning-recommendation-system-part-2-deep-learning-for-recommender-systems/ 6. How to Build a Winning Deep Learning Powered Recommender System-Part 3 https://developer.nvidia.com/blog/how-to-build-a-winning-deep-learning-powered-recommender-system-part-3/ 7. Learn more about NVIDIA Accelerated Data Science https://www.nvidia.com/en-us/ai-data-science/ About our presenters: Host Jim Scott, head of developer relations, data science, at NVIDIA. Over his career, he has focused on enabling business to solve the most complex engineering and data related problems. His expertise in blending business needs with technology to drive innovation has influenced every major industry. Chris Deotte, senior data scientist at NVIDIA. Chris has a Ph.D. in computational science and mathematics with a thesis on optimizing parallel processing. Chris is a 4x Kaggle grandmaster. Bo Liu holds a Ph.D. in Applied Math and Statistics from Johns Hopkins University. Bo spent time working in Fintech and while doing so was competing in his free time and earning his Kaggle Grandmaster's title. Bo joined NVIDIA in May of 2020 and is mostly interested in deep learning competitions, especially those related to computer vision. Gilberto Titericz, known as Giba is currently a senior data scientist at NVIDIA. Prior to NVIDIA, he worked at Ople, Airbnb, Petrobras, and Siemens. Gilberto had held the #1 position at Kaggle for more than two years. Benedikt Schifferer, is a deep learning engineer at NVIDIA working on recommender systems. He holds a masters of science in data science from Columbia University, and previously developed recommender systems for a German ecommerce company.