Real-Time Bidding (RTB) System Design

Large-Scale Online Ads Ranking and Auction System

Design a high-throughput ads ranking system for a social media platform with 500M DAU. The system must select the most relevant ads from a corpus of 10M candidates within a 100ms P99 latency budget. Focus on the end-to-end ML lifecycle: from real-time feature engineering (handling user signals) and multi-stage funnel architecture (retrieval and ranking), to specialized ad-tech challenges like position bias, click/conversion calibration for auctions, and handling delayed feedback loops in conversion data. Discuss the trade-offs between model complexity and serving constraints, and how to ensure the system maximizes long-term revenue (eCPM) while maintaining advertiser trust through accurate ROI predictions.
Wide & DeepDeepFMTwo-Tower ModelFAISSHNSWKafkaFlinkSparkRedisPlatt ScalingIsotonic RegressionMulti-Task Learning
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Real-Time Bidding (RTB) System Design

Design a high-scale Real-Time Bidding (RTB) system for a Demand-Side Platform (DSP). The system must process over 1 million bid requests per second from global ad exchanges with a strict P99 latency of 50ms. Detail the architecture for predicting Click-Through Rate (CTR) and Conversion Rate (CVR), incorporating real-time budget pacing, handling delayed conversion feedback, and ensuring model calibration for accurate financial bidding. Explain your choices for data ingestion, low-latency feature retrieval, and the trade-offs between model complexity and inference speed in a production environment.
Factorization MachinesField-aware Factorization MachinesLogistic RegressionKafkaFlinkRedisSparkPlatt ScalingIsotonic RegressionPID ControllerProtobufFeast
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