Optimizing deep learning recommender systems training on cpu cluster architectures
During the last two years, the goal of many researchers has been to squeeze the last bit of
performance out of HPC system for AI tasks. Often this discussion is held in the context of …
performance out of HPC system for AI tasks. Often this discussion is held in the context of …
Persia: An open, hybrid system scaling deep learning-based recommenders up to 100 trillion parameters
Recent years have witnessed an exponential growth of model scale in deep learning-based
recommender systems---from Google's 2016 model with 1 billion parameters to the latest …
recommender systems---from Google's 2016 model with 1 billion parameters to the latest …
PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems
The development of personalized recommendation has significantly improved the accuracy
of information matching and the revenue of e-commerce platforms. Recently, it has two …
of information matching and the revenue of e-commerce platforms. Recently, it has two …
Software-hardware co-design for fast and scalable training of deep learning recommendation models
Deep learning recommendation models (DLRMs) have been used across many business-
critical services at Meta and are the single largest AI application in terms of infrastructure …
critical services at Meta and are the single largest AI application in terms of infrastructure …
GPU accelerated boosted trees and deep neural networks for better recommender systems
C Deotte, B Liu, B Schifferer, G Titericz - Proceedings of the …, 2021 - dl.acm.org
In this paper we present our 1st place solution of the ACM RecSys 2021 challenge. Twitter
provided a dataset of around 1 billion tweets-user pairs to develop models predicting user …
provided a dataset of around 1 billion tweets-user pairs to develop models predicting user …
GPU accelerated feature engineering and training for recommender systems
B Schifferer, G Titericz, C Deotte, C Henkel… - Proceedings of the …, 2020 - dl.acm.org
In this paper we present our 1st place solution of the RecSys Challenge 2020 which focused
on the prediction of user behavior, specifically the interaction with content, on this year's …
on the prediction of user behavior, specifically the interaction with content, on this year's …
Hyperparameter learning for deep learning-based recommender systems
Deep learning (DL)-based recommender system (RS), particularly for its advances in the
recent five years, has been startling. It reshapes the architectures of traditional RSs by lifting …
recent five years, has been startling. It reshapes the architectures of traditional RSs by lifting …
InTune: Reinforcement learning-based data pipeline optimization for deep recommendation models
K Nagrecha, L Liu, P Delgado… - Proceedings of the 17th …, 2023 - dl.acm.org
Deep learning-based recommender models (DLRMs) have become an essential component
of many modern recommender systems. Several companies are now building large compute …
of many modern recommender systems. Several companies are now building large compute …
Classification-based deep neural network architecture for collaborative filtering recommender systems
J Bobadilla, F Ortega, A Gutiérrez, S Alonso - 2020 - reunir.unir.net
This paper proposes a scalable and original classification-based deep neural architecture.
Its collaborative filtering approach can be generalized to most of the existing recommender …
Its collaborative filtering approach can be generalized to most of the existing recommender …
Centaur: A chiplet-based, hybrid sparse-dense accelerator for personalized recommendations
Personalized recommendations are the backbone machine learning (ML) algorithm that
powers several important application domains (eg, ads, e-commerce, etc) serviced from …
powers several important application domains (eg, ads, e-commerce, etc) serviced from …