Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System

Approximate nearest neighbor (ANN) search is widely used in the retrieval stage of large-scale recommendation systems. In this stage, candidate items are indexed using their learned embedding vectors, and ANN search is executed for each user (or item) query to retrieve a set of relevant items. However, ANN-based retrieval has two key limitations. First, item embeddings and their indices are typically learned in separate stages: indexing is often performed offline after embeddings are trained, which can yield suboptimal retrieval quality-especially for newly created items. Second, although ANN offers sublinear query time, it must still be run for every request, incurring substantial computation cost at industry scale. In this paper, we propose MultiFaceted Learnable Index (MFLI), a scalable, real-time retrieval paradigm that learns multifaceted item embeddings and indices within a unified framework and eliminates ANN search at serving time. Specifically, we construct a multifaceted hierarchical codebook via residual quantization of item embeddings and co-train the codebook with the embeddings. We further introduce an efficient multifaceted indexing structure and mechanisms that support real-time updates. At serving time, the learned hierarchical indices are used directly to identify relevant items, avoiding ANN search altogether. Extensive experiments on real-world data with billions of users show that MFLI improves recall on engagement tasks by up to 11.8\%, cold-content delivery by up to 57.29\%, and semantic relevance by 13.5\% compared with prior state-of-the-art methods. We also deploy MFLI in the system and report online experimental results demonstrating improved engagement, less popularity bias, and higher serving efficiency.

SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs

Serving deep learning based recommendation models (DLRM) at scale is challenging. Existing systems rely on CPU-based ANN indexing and filtering services, suffering from non-negligible costs and forgoing joint optimization opportunities. Such inefficiency makes them difficult to support more complex model architectures, such as learned similarities and multi-task retrieval. In this paper, we propose SilverTorch, a model-based system for serving recommendation models on GPUs. SilverTorch unifies model serving by replacing standalone indexing and filtering services with layers of served models. We propose a Bloom index algorithm on GPUs for feature filtering and a tensor-native fused Int8 ANN kernel on GPUs for nearest neighbor search. We further co-design the ANN search index and filtering index to reduce GPU memory utilization and eliminate unnecessary computation. Benefit from SilverTorch's serving paradigm, we introduce a OverArch scoring layer and a Value Model to aggregate results across multi-tasks. These advancements improve the accuracy for retrieval and enable future studies for serving more complex models. For ranking, SilverTorch's design accelerates item embedding calculation by caching the pre-calculated embeddings inside the serving model. Our evaluation on the industry-scale datasets show that SilverTorch achieves up to 5.6x lower latency and 23.7x higher throughput compared to the state-of-the-art approaches. We also demonstrate that SilverTorch's solution is 13.35x more cost-efficient than CPU-based solution while improving accuracy via serving more complex models. SilverTorch serves over hundreds of models online across major products and recommends contents for billions of daily active users.