Neural IR

What is Neural IR?

Neural Information Retrieval applies neural models to search, producing vector representations (embeddings) for queries and documents and/or neural re-rankers that score top candidates with deep context.

How It Works (quick)

• Dense retrieval: Dual-encoder encodes documents and queries into vectors; find nearest neighbors with an ANN index.
• Re-ranking: Cross-encoders (or late interaction models) rescore the top N from lexical/dense recall using full token interactions.
• Hybrid pipeline: Lexical recall (BM25) + dense recall (vectors) → neural re-rank → business-aware ranking.
• Training: Contrastive learning on click/purchase pairs; hard negative mining; domain adaptation.
• Serving: Batch/stream embeddings, cache hot queries, quantize/distill for latency.

Why It Matters in E-commerce

• Semantic match: Captures meaning beyond exact wording (“rainproof trail runners” ↔ waterproof trail shoes).
• Recall rescue: Finds items when brand/model terms don’t align with catalog text.
• Long queries: Handles descriptive, multi-constraint queries better than pure lexical.

Best Practices

• Start hybrid: Keep BM25 candidates; add dense recall where coverage is weak.
• Domain tuning: Train/fine-tune on your catalog and queries; guard brand/SKU exactness with keyword fields.
• Latency budgets: Use ANN with product-level caching; distill/quantize models; cap re-rank set size.
• Freshness: Schedule embedding refreshes; compute on publish/update events.
• Safety & bias: Enforce hard filters (stock/ACL), brand caps, and diversity before final rank.
• Measurement: NDCG@k, CTR, conversion, zero-results recovery, and tail latency.

Challenges

• Compute cost, embedding staleness, multilingual tuning, explainability, and cold-start items.

Examples

• Query “eco-friendly running jacket that packs small” retrieves packable recycled-fabric shells even without the exact words.
• Dense recall lifts relevant compatible accessories despite sparse titles.

Summary

Neural IR adds meaning to retrieval. Run it in a hybrid stack with strict guardrails, tuned embeddings, and fast ANN to lift recall and relevance without sacrificing speed or control.