Distilling Long-Tail User Behavior into Scalable Embeddings for Job Search

Indeed’s engineering team describes a user behavior modeling system that learns from long-term user histories offline and distills them into compact user embeddings for job search ranking. The system addresses the tension between using rich behavioral data and maintaining millisecond-level latency in production ranking systems.