Unraveling Rovo search relevance

This post details the technical implementation of Rovo search relevance, including the use of OpenSearch with BM25 and KNN for initial retrieval, a Gradient Boosted Decision Tree model for L1 reranking, and a multi-task trained cross-encoder and DCN model for L2 semantic and behavioral ranking. It also describes the L3 interleaving mechanism based on product affinity and query intent. The Smart Answers workflow is explained, including query classification and rewriting with enriched context. The post also touches upon the federated search approach for certain third-party applications.