![]() Recommender systems are trained using data gathered about the users, items, and their interactions, which include impressions, clicks, likes, mentions, and so on. FE: feature engineering PP: preprocessing ETL: extract-transform-load. End-to-end recommender system architecture. Recommender systems overviewįigure 1 shows an example end-to-end recommender system architecture.įigure 1. Merlin also enables low latency, high-throughput, production inference.īefore diving into Merlin, we discuss more about the challenges that large-scale recommender systems are facing today. 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. ![]() To meet the computational demands for large-scale DL recommender systems training and inference, NVIDIA introduces Merlin. 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.Ĭonsequently, 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. DL recommender models are built upon existing techniques such as embeddings to handle categorical variables and factorization to model the interactions between variables. 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. A 1% improvement in the quality of recommendations can translate into billions of dollars in revenue. On some of the largest commercial platforms, recommendations account for as much as 30% of the revenue. 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. 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.
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