Dense Retrieval (Bi-encoder)

What is Dense Retrieval?

Dense retrieval uses bi-encoders to map queries and documents into the same vector space. Similarity (dot product/cosine) retrieves top candidates efficiently.

How It Works (quick)

• Train: Contrastive learning on query–doc pairs.
• Encode: Precompute doc vectors; encode queries at runtime.
• ANN search: Return nearest neighbors; filter; pass to ranker.
• Cold-start: Use synthetic pairs if labels are scarce.

Why It Matters in E-commerce

• Great for long-tail and language variance.
• Low latency with precomputed vectors.

Best Practices

• Fine-tune on catalog + query logs.
• Refresh embeddings when inventory changes.
• Pair with neural re-rankers for quality.

Summary

Dense retrieval is fast semantic recall. Tune on your domain and combine with re-ranking.

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