Personalized Restaurant Recommendation and Ranking System

Event Discovery and Recommendation System

Design a high-scale event recommendation system for a platform similar to Eventbrite. The system must handle a corpus of millions of ephemeral, location-dependent events and 50M+ users. Detail the end-to-end ML lifecycle, focusing on how you would address the cold-start problem for new events, ensure low-latency geospatial retrieval, and rank candidates based on booking probability. Discuss the trade-offs between model complexity and inference speed, and explain your strategy for handling delayed feedback and temporal data leakage in your training pipeline.
Two-TowerLightGBMFAISSKafkaFlinkSparkSBERTH3RedisMMoE
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Personalized Restaurant Recommendation and Ranking System

Design a high-scale recommendation and ranking system for a global food delivery platform. The system must handle over 100 million users and a million restaurants, delivering personalized results within a 200ms latency budget. Focus on the end-to-end ML lifecycle, specifically addressing geo-spatial constraints, real-time context (like weather and ETA), multi-stage ranking (retrieval vs. ranking), and the handling of the extreme feedback loop inherent in food ordering behavior.
Two-TowerDeepFMMMoEFAISSH3XGBoostLightGBMKafkaFlinkSparkRedis
00
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