Beyond Keyword Matching
Traditional search relies on exact keyword matching. AI-powered search understands what customers mean, not just what they type.
Key Technologies
Natural Language Processing (NLP)
NLP enables understanding of:
- Synonyms and related terms
- Spelling variations and typos
- Contextual meaning
- Query intent classification
Vector Search
Embeddings represent products and queries as vectors in high-dimensional space. Similar items cluster together, enabling semantic similarity search.
// Example: Vector search with embeddings
const queryEmbedding = await model.embed("comfortable running shoes");
const results = await vectorDB.search({
vector: queryEmbedding,
limit: 20,
filter: { category: "footwear" }
});Learning to Rank
Machine learning models optimize result ordering based on:
- Click-through rates
- Conversion data
- User behavior patterns
- Product attributes
Implementation Architecture
- Query Processing: Parse, normalize, and classify intent
- Retrieval: Fetch candidates using hybrid search
- Ranking: Apply ML models to order results
- Personalization: Adjust based on user context
- Display: Render with facets and filters
Measuring Success
- Search conversion rate
- Zero-result searches
- Click position (higher is better)
- Search abandonment rate
- Revenue per search
Popular Tools
Consider these platforms:
- Algolia: Developer-friendly, fast, AI-ready
- Elasticsearch: Open-source, highly customizable
- Typesense: Open-source alternative to Algolia
- Pinecone: Vector database for embeddings
Sarah Chen
AI researcher and e-commerce strategist specializing in machine learning applications for retail.
