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 …
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 …
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 …
Accelerating recommendation system training by leveraging popular choices
Recommender models are commonly used to suggest relevant items to a user for e-
commerce and online advertisement-based applications. These models use massive …
commerce and online advertisement-based applications. These models use massive …
Merlin hugeCTR: GPU-accelerated recommender system training and inference
Z Wang, Y Wei, M Lee, M Langer, F Yu, J Liu… - Proceedings of the 16th …, 2022 - dl.acm.org
In this talk, we introduce Merlin HugeCTR. Merlin HugeCTR is an open source, GPU-
accelerated integration framework for click-through rate estimation. It optimizes both training …
accelerated integration framework for click-through rate estimation. It optimizes both training …
[PDF][PDF] Merlin: a gpu accelerated recommendation framework
E Oldridge, J Perez, B Frederickson… - Proceedings of …, 2020 - academia.edu
Merlin: A GPU Accelerated Recommendation Framework Page 1 Merlin: A GPU Accelerated
Recommendation Framework Even Oldridge∗ Julio Perez Ben Frederickson Nicolas …
Recommendation Framework Even Oldridge∗ Julio Perez Ben Frederickson Nicolas …
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 …
Beta-rec: Build, evaluate and tune automated recommender systems
The field of recommender systems has rapidly evolved over the last few years, with
significant advances made due to the in-flux of deep learning techniques. However, as a …
significant advances made due to the in-flux of deep learning techniques. However, as a …
Fleche: an efficient GPU embedding cache for personalized recommendations
Deep learning based models have dominated current production recommendation systems.
However, the gap between CPU-side DRAM data accessing and GPU processing still …
However, the gap between CPU-side DRAM data accessing and GPU processing still …
Tenrec: A large-scale multipurpose benchmark dataset for recommender systems
Existing benchmark datasets for recommender systems (RS) either are created at a small
scale or involve very limited forms of user feedback. RS models evaluated on such datasets …
scale or involve very limited forms of user feedback. RS models evaluated on such datasets …