Announcing NVIDIA Merlin: An Application Framework for Deep Recommender Systems [NVIDIA Blog]

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, recommendations account for as much as 30% of the revenue. A 1% improvement in the quality of recommendations can translate into billions of dollars in revenue.

With the rapid growth in scale of industry datasets, deep learning (DL) recommender models, which capitalize on very large amounts of training data, have started to gain advantages over traditional methods such as content-based, neighborhood, and latent factor methods. DL recommender models are built upon existing techniques such as embeddings to handle categorical variables and factorization to model the interactions between variables. However, they also tap into the vast and rapidly growing literature on novel network architectures and optimization algorithms to build and train more expressive models.

Consequently, the combination of more sophisticated models and rapid data growth has raised the bar for computational resources required for training while also placing new burdens on production systems. To meet the computational demands for large-scale DL recommender systems training and inference, NVIDIA introduces Merlin.

Merlin is an end-to-end recommender-on-GPU framework that aims to provide fast feature engineering and high training throughput to enable fast experimentation and production retraining of DL recommender models. Merlin also enables low latency, high-throughput, production inference.

Before diving into Merlin, we discuss more about the challenges that large-scale recommender systems are facing today.

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