Large-Scale Recommendation Ranking for Long User Sequences

Large-Scale Recommendation Ranking for Long User Sequences

Design the final-stage ranking system for a content discovery platform (like Pinterest) where users have long-term interaction histories (1,000+ events). Your system must efficiently handle these sequences to capture both long-term and short-term interests within a 100ms P99 latency budget. Detail the data and feature pipelines, the specific attention mechanisms used to overcome the computational cost of long sequences, and how the system maintains online/offline consistency for embedding-based features.
SIMDINDCN-v2TransformerKafkaFlinkSparkTectonPyTorchHorovod
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