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

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 …

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

Recommendation systems are of crucial importance for a variety of modern apps and web
services, such as news feeds, social networks, e-commerce, search, etc. To achieve peak …

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

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

The embedding-based representation learning is commonly used in deep learning
recommendation models to map the raw sparse features to dense vectors. The traditional …

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 …

Building a scalable and real-time recommendation system is vital for many businesses
driven by time-sensitive customer feedback, such as short-videos ranking or online ads …

Deep learning Recommendation Models (DLRMs) plays an important role in various
application domains. However, existing DLRM training systems require a large number of …