Recommender systems (RecSys) have become a key component in many online services, such as e-commerce, social media, news service, or online video streaming. However with the growth in importance, the growth in scale of industry datasets, and more sophisticated models, the bar has been raised for computational resources required for recommendation systems. To meet the computational demands…
]]>This post was originally published on the RAPIDS AI blog. In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we can explore the performance of deep learning on tabular data and compare it to the xgboost method. Deep learning is…
]]>Recently, NVIDIA CEO Jensen Huang announced updates to the open beta of NVIDIA Merlin, an end-to-end framework that democratizes the development of large-scale deep learning recommenders. With NVIDIA Merlin, data scientists, machine learning engineers, and researchers can accelerate their entire workflow pipeline from ingesting and training to deploying GPU-accelerated recommenders (Figure 1).
]]>Recommender systems are ubiquitous in online platforms, helping users navigate through an exponentially growing number of goods and services. These models are key in driving user engagement. With the rapid growth in scale of industry datasets, deep learning (DL) recommender models have started to gain advantages over traditional methods by capitalizing on large amounts of training data.
]]>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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