Deep learning based models have dominated current production recommendation systems.
However, the gap between CPU-side DRAM data accessing and GPU processing still …

Personalized recommendation models (RecSys) are one of the most popular machine
learning workload serviced by hyperscalers. A critical challenge of training RecSys is its …

The development of personalized recommendation has significantly improved the accuracy
of information matching and the revenue of e-commerce platforms. Recently, it has two …

Deep learning recommendation models (DLRMs) are typically composed of two sets of
parameters: large embedding tables to handle sparse categorical inputs, and neural …

Recommender models are commonly used to suggest relevant items to a user for e-
commerce and online advertisement-based applications. These models use massive …

Recommendation models rely on deep learning networks and large embedding tables,
resulting in computationally and memory-intensive processes. These models are typically …

Personalized recommendations are the backbone machine learning (ML) algorithm that
powers several important application domains (eg, ads, e-commerce, etc) serviced from …

Deep Learning Recommendation Models (DLRMs) are very popular in personalized
recommendation systems and are a major contributor to the data-center AI cycles. Due to the …

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 …

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 …