Sept 27 - Oct 1, 2021
Join NVIDIA online at the 15th annual ACM Conference on Recommender Systems (RecSys 2021) to see how new research results, methods, libraries, and techniques in the broad field of recommender systems are driving our future. We’ll feature our own virtual theater with speakers from a variety of leading industries and domains, along with demos, related content, and more.
Explore NVIDIA’s Transformers4Rec—an open-source library built on HuggingFace’s Transformers library that makes advances of NLP-based transformers to the recommender system community for sequential and session-based recommendation tasks. This paper demonstrates Transformers4Rec's usefulness and the applicability of transformer architectures in next-click prediction for user sessions, where sequence lengths are much shorter than those commonly found in NLP.
At RecSys 2021, you can explore a range of groundbreaking work in the field of recommender systems. Take a closer look at the scheduled NVIDIA sessions that are part of this year’s program and join us at either of our coffee breaks to be entered into a drawing to win a GeForce RTX™ 3090.
Don’t miss a session. Register now for RecSys 2021.
Hear from industry leaders, data scientists, and engineers as they explain their groundbreaking work for building, deploying, and optimizing recommender systems.
NVIDIA’s Transformers4Rec library helps machine learning engineers and data scientists explore and apply transformers architectures when building sequential and session-based recommendation pipelines. Register for our session at RecSys 2021 or download our paper to learn more.
In this paper, we present our first-place solution of the ACM RecSys 2021 challenge. Our final submission is an ensemble of stacked models using in total five XGBoost models and three neural networks. Register for our session at RecSys 2021 or download our paper to learn more.
NVIDIA Merlin™ optimized embedding implementation is up to 8x more performant than other frameworks’ embedding layers and is available as a TensorFlow (TF) Plugin. It works seamlessly with TF and as a drop-in replacement for native TF embedding layers.
In this blog, we cover sequential and session-based recommendation tasks, including why it’s important and practical use-cases. We also provide a brief overview of our Transformers4Rec solution.
Learn about NVIDIA Merlin, an open source framework for building recommender systems. Merlin empowers data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale.
Industry challenges help advance the recommender system field for everyone. This year, NVIDIA’s wins are fueling ideas for new techniques into recsys frameworks like NVIDIA Merlin.
Deep learning methods provide better prediction at scale for building recommenders. Learn how these methods used by our award-winning recommender system team could be the key to optimizing your recommender system.
Read about the latest trends for recommender system practices within industry. The whitepaper includes insights from industry leads from companies such as Tencent, Meituan, Wayfair, Magazine Luiza, The New York Times, and more
This year, the NVIDIA KGMON, Merlin, and RAPIDS.AI team earned first place in the ACM RecSys2021 Challenge, SIGIR eCom Data Challenge, and Booking.com Challenge. Congratulations to all of our participants!
Benedikt Schifferer Chris Deotte Jean-Francois Puget Gabriel de Souza Pereira Moreira Gilberto Titericz Jiwei Liu Ronay Ak
Learn about their winning solution.
Chris Deotte Bo Liu Benedikt Schifferer Gilberto Titericz Ronay Ak Jiwei Liu Gabriel De Souza Pereira Moreira
Ronay Ak Sara Rabhi Md Yasin Kabir Gabriel Moreira
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