Recommendation systems are widely used today to personalize user experiences and improve customer engagement in various settings like e-commerce, social media, and news feeds. Serving user requests with low latency and high accuracy is critical to sustaining user engagement. This includes performing high-speed lookups and computations while seamlessly refreshing models with the newest…
]]>NVIDIA Merlin is an open beta application framework and ecosystem that enables the end-to-end development of recommender systems, from data preprocessing to model training and inference, all accelerated on NVIDIA GPU. We announced Merlin in a previous post and have been continuously making updates to the open beta. In this post, we detail the new features added to the open beta NVIDIA Merlin…
]]>Click-through rate (CTR) estimation is one of the most critical components of modern recommender systems. As the volume of data and its complexity grow rapidly, the use of deep learning (DL) models to improve the quality of estimations has become widespread. They generally have greater expressive power than traditional machine learning (ML) approaches. Frequently evolving data also implies that…
]]>Recommender systems drive every action that you take online, from the selection of this web page that you’re reading now to more obvious examples like online shopping. They play a critical role in driving user engagement on online platforms, selecting a few relevant goods or services from the exponentially growing number of available options. On some of the largest commercial platforms…
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