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 …
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 …
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 …
[PDF][PDF] Persia: a hybrid system scaling deep learning based recommenders up to 100 trillion parameters
Deep learning based models have dominated the current landscape of production
recommender systems. Furthermore, recent years have witnessed an exponential growth of …
recommender systems. Furthermore, recent years have witnessed an exponential growth of …
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 …
Collaboration-aware graph convolutional network for recommender systems
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems
by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless …
by virtue of the message-passing that implicitly captures collaborative effect. Nevertheless …
[HTML][HTML] Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural Network
Recommendation systems are vital tools for helping users discover content that suits their
interests. Collaborative filtering methods are one of the techniques employed for analyzing …
interests. Collaborative filtering methods are one of the techniques employed for analyzing …
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 …
Star-gcn: Stacked and reconstructed graph convolutional networks for recommender systems
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-
GCN) architecture to learn node representations for boosting the performance in …
GCN) architecture to learn node representations for boosting the performance in …