Submitted by marcosluis2186 t3_yuk9ga in MachineLearning

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.

Despite the ubiquitous adoption of production-scale deep learning frameworks like TensorFlow or PyTorch, these general-purpose frameworks fall short of business demands in recommendation scenarios for various reasons: on one hand, tweaking systems based on static parameters and dense computations for recommendation with dynamic and sparse features is detrimental to model quality; on the other hand, such frameworks are designed with batch-training stage and serving stage completely separated, preventing the model from interacting with customer feedback in real-time.

These issues led us to reexamine traditional approaches and explore radically different design choices. In this paper, we present Monolith, a system tailored for online training.

Our design has been driven by observations of our application workloads and production environment that reflects a marked departure from other recommendations systems.

Our contributions are manifold: first, we crafted a collisionless embedding table with optimizations such as expirable embeddings and frequency filtering to reduce its memory footprint; second, we provide an production-ready online training architecture with high fault-tolerance; finally, we proved that system reliability could be traded-off for real-time learning. Monolith has successfully landed in the BytePlus Recommend product.

Read more: https://arxiv.org/abs/2209.07663

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fhadley t1_iwbffzm wrote

I'm a big fan of how they don't mention the product that this paper may or may not mostly underpin until the last sentence of the abstract. Like idk what exactly to glean from that fact, but I sure do like it.

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