TLDR Tech / Data
Beyond BM25 and dense embeddings: How we built smart and interpretable retrieval at Faire
Faire deployed a sparse neural retrieval model to solve vocabulary mismatch in marketplace search while keeping Elasticsearch compatibility and interpretability. By expanding queries and documents with semantically related terms, the system improved long-tail candidate quality by over 30%, lifted search-page order value by 4.27%, and increased global marketplace order value. Key engineering choices included domain-specific BERT pretraining, WordPiece tokenization, max pooling, asymmetric sparsity penalties, and moving Product Quality Score blending to index time to preserve latency.
Read the full story