In a vector-based search engine, reranking is a step that improves the quality of search results by reordering the initial candidate matches. When a query is processed, the system first retrieves a set of approximate matches using efficient vector similarity algorithms like approximate nearest neighbor (ANN) search. This initial step prioritizes speed over precision to quickly narrow down potential results. Reranking then applies a more precise—but computationally heavier—similarity calculation to the top candidates, ensuring the final results better match the query’s intent. For example, while the initial search might use cosine similarity with compressed vectors, reranking could recompute distances using higher-dimensional vectors or a different similarity metric tailored to the data.
A common example involves text search: suppose a user searches for “durable running shoes.” The initial ANN search might return products with vectors close to the query vector, but some results could be tangentially related (e.g., “hiking boots” or “shoe polish”). During reranking, a cross-encoder model—a type of neural network that evaluates pairs of text sequences—might analyze the query and each candidate’s description to compute a relevance score. This model can detect nuanced relationships, like whether “durable” strongly aligns with a product’s material description. Reranking might also incorporate business logic, such as boosting products with higher ratings or filtering out-of-stock items, which the initial vector search couldn’t handle.
From a developer’s perspective, reranking involves trade-offs. The reranking model (e.g., BERT for text or a custom metric for images) must balance accuracy and latency. For instance, reranking the top 100 results with a slow model might add 100ms of latency, so engineers often limit reranking to the top 20-50 candidates. Libraries like FAISS or Annoy handle the initial ANN search, while frameworks like Sentence-Transformers or PyTorch support reranking models. By separating retrieval and reranking, systems maintain scalability—fast approximate search for broad matching, precise reranking for final refinement—without overwhelming computational resources. This two-stage approach is widely adopted in production systems, from e-commerce search to recommendation engines.