Ad Ranking and Revenue Optimization System

Ad Ranking and Revenue Optimization System

Design a high-scale, low-latency ads ranking system capable of selecting the most relevant ads from a corpus of 100M+ candidates. The design must address the dual goals of maximizing platform revenue (eCPM) and user experience. Specifically, detail the multi-stage retrieval and ranking pipeline, strategies for handling extreme feature sparsity, techniques for model calibration to support fair auctions, and mechanisms to mitigate position bias and handle delayed feedback from conversion events. Discuss the system architecture required to support 50k+ QPS with sub-100ms P99 latency while ensuring offline-online feature consistency.
DeepFMTwo-Tower ModelIsotonic RegressionNegative DownsamplingFAISSKafkaFlinkSparkFeastTriton Inference Server
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