Showing 1–14 of 14 results
/ Date/ Name
Apr 12, 2021Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation ModelsJul 12, 2023Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads RankingMay 26, 2021Low-Precision Hardware Architectures Meet Recommendation Model Inference at ScaleOct 16, 2020Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary DataFeb 20, 2025External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads RecommendationJan 23, 2025Personalized Interpolation: Achieving Efficient Conversion Estimation with Flexible Optimization WindowsFeb 10, 2026Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture DesignOct 2, 2025Improving Large-Scale Recommender Systems with Auxiliary LearningMar 11, 2022DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate PredictionApr 13, 2026SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference ScalingDec 9, 2025Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads RecommendationsMar 4, 2024Wukong: Towards a Scaling Law for Large-Scale RecommendationMar 1, 2024Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale RecommendationMar 21, 2026Implicit Turn-Wise Policy Optimization for Proactive User-LLM Interaction