Recommenders / Personalization

Upcoming DL RecSys Summit: Develop and Optimize Deep Learning Recommender Systems

The NVIDIA, Facebook, and TensorFlow recommender teams will be hosting a summit with live Q&A to dive into best practices and insights on how to develop and optimize deep learning recommender systems.

Develop and Optimize Deep Learning Recommender Systems
Thursday, July 29 at 10 a.m. PT

By joining this Deep Learning Recommender Summit, you will hear from fellow ML engineers and data scientists from NVIDIA, Facebook, and TensorFlow on best practices, learnings, and insights for building and optimizing highly effective DL recommender systems.

Sessions include:

High-Performance Recommendation Model Training at Facebook
In this talk, we will first analyze how model architecture affects the GPU performance and efficiency, and also present the performance optimizations techniques we applied to improve the GPU utilization, which includes optimized PyTorch-based training stack supporting both model and data parallelism, high-performance GPU operators, efficient embedding table sharding, memory hierarchy and pipelining.

RecSys2021 Challenge: Predicting User Engagements with Deep Learning Recommender Systems
The NVIDIA team, a collaboration of Kaggle Grandmaster and NVIDIA Merlin, won the RecSys2021 challenge. It was hosted by Twitter, who provided almost 1 billion tweet-user pairs as a dataset. The team will present their winning solution with a focus on deep learning architectures and how to optimize them.

Revisiting Recommender Systems on GPU
A new era of faster ETL, Training, and Inference is coming to the RecSys space and this talk will walk through some of the patterns of optimization that guide the tools we are building to make recommenders faster and easier to use on the GPU.

TensorFlow Recommenders
TensorFlow Recommenders is an end-to-end library for recommender system models: from retrieval, through ranking, to post-ranking. In this talk, we describe how TensorFlow Recommenders can be used to fit and safely deploy sophisticated recommender systems at scale.

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