Hybrid Search
Search approach that combines multiple search techniques, typically keyword and semantic search, to deliver the best possible results.
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Start Free TrialView PricingHere’s a dilemma: keyword search is great for exact matches but struggles with natural language. Semantic search understands meaning but sometimes misses obvious exact matches. So which one should you use?
The answer is both. This is hybrid search, combining the strengths of different search approaches to cover all types of queries effectively.
Think of it like having two shop assistants with different skills. One knows the exact location of every product by code and name. The other understands what customers need even when they describe it vaguely. Together, they can help any customer, whether they know exactly what they want or need guidance to figure it out.
Why you need both types of search
Let me show you why neither keyword nor semantic search alone is enough.
A customer searches for “Nike Air Max 270 black size 42”. This is specific and exact. They know the product code, color, and size. Keyword search handles this perfectly, finding exact matches for those specific terms. Semantic search might be too clever here, showing “similar Nike shoes” when the customer wants exactly that one product.
Another customer searches for “comfortable walking shoes for long days on my feet”. This is descriptive and intent-driven. Keyword search struggles because your products probably don’t contain the exact phrase “long days on my feet”. Semantic search shines here, it understands the customer wants cushioned, supportive shoes designed for extended wear.
A third customer searches for “SKU-12345”. This is a product code. Keyword search finds it instantly. Semantic search might get confused, it’s trying to understand meaning, but this is just an identifier.
These three searches require different approaches. Hybrid search handles all three by using both techniques together, letting each do what it does best.
How hybrid search actually works
When you search with a hybrid system, here’s what happens behind the scenes:
Both keyword and semantic search run at the same time. They’re happening in parallel, so there’s minimal speed impact. Each produces a list of relevant products with scores.
Keyword search might return: Product A (95 points), Product C (87 points), Product F (82 points)…
Semantic search might return: Product D (92 points), Product A (88 points), Product B (85 points)…
Notice Product A appears in both lists, but with different scores. This is common, a product can match both ways but with different strengths.
Now the system combines these results. This is where it gets interesting. Different products appear in different lists, some in both, some in just one. The system needs to create one unified ranking.
There are several ways to do this combination, but the most common is weighted scoring. The system might decide: for this query, give 60% weight to semantic scores and 40% to keyword scores. Product A’s final score becomes: (88 × 0.6) + (95 × 0.4) = 90.8 points.
This weighting can be adjusted based on the type of query. Queries that look exact (“Nike Air Max”) get more keyword weight. Natural language queries (“shoes for running”) get more semantic weight.
When keyword search takes the lead
Certain searches clearly need exact matching more than concept understanding:
Brand-specific searches like “Adidas Ultraboost” should match exactly those products. The customer isn’t exploring, they know what they want. Keyword search ensures precision.
Product codes and SKUs need exact matches. “Model-XYZ-123” isn’t a concept to interpret; it’s an identifier to find. Keyword search handles this instantly.
Technical specifications benefit from exact matching. “16GB RAM laptop” should prioritize laptops with exactly 16GB of RAM, not similar amounts.
For these queries, hybrid search gives keyword results more weight while still using semantic understanding to fill gaps if needed.
When semantic search takes over
Other searches need concept understanding more than exact matching:
Descriptive searches like “warm coat for winter” benefit from semantic understanding. Your products might say “insulated jacket” or “thermal outerwear”, semantic search connects these concepts.
Intent-based searches like “gift for coffee lover” require understanding what makes something gift-appropriate and coffee-related. Keyword matching alone finds products containing those words; semantic search finds actually relevant gifts.
Complex need-based searches like “laptop for video editing under $1500” combine multiple concepts. Semantic search understands “for video editing” means high performance, good graphics, specific specs, not just any laptop mentioning video.
For these queries, hybrid search emphasizes semantic results while keyword search provides a relevance baseline.
The best of both worlds
Here’s what makes hybrid search powerful: you get precision when you need it and intelligence when you need that.
A customer types “running shoes”. Both keyword and semantic search contribute. Keyword ensures products literally called “running shoes” appear. Semantic adds products described as “athletic footwear” or “jogging sneakers” that keyword alone would miss.
Another types “shoes for standing all day”. Keyword finds products mentioning these words. Semantic understands the customer needs cushioning and support, finding products with these attributes even if they don’t mention “standing all day”.
A third types “Nike Air Max 90 white size 10”. Keyword dominates, finding exactly that product. Semantic provides backup, if that exact configuration is unavailable, it suggests close alternatives in the same product line.
This combination handles the full spectrum of how customers actually search, from precise to exploratory.
Adjusting the balance
The magic of hybrid search is in the weighting, how much emphasis to put on each technique.
Static weighting uses fixed proportions: always 50% keyword, 50% semantic. This is simple but not optimal for all queries.
Query-based weighting adjusts automatically. The system analyzes the query: Does it look like a product code? More keyword weight. Is it a question? More semantic weight. Does it contain brand names? Balance toward keyword.
Learned weighting uses machine learning. The system observes which combination of keyword and semantic results leads to clicks and purchases for different query types, then optimizes the weights accordingly.
Most sophisticated systems use learned weighting, continuously improving the balance based on what actually works for your customers and your catalog.
What this means for your webshop
Hybrid search means customers get good results regardless of how they search. Whether they know exactly what they want or need help figuring it out, whether they use technical terms or describe needs in plain language, the search works.
This versatility is why hybrid search typically improves conversion rates by 15-25% compared to keyword-only search. It handles more query types successfully, leading to fewer frustrated searches and more purchases.
Modern search solutions like TextAtlas use hybrid search by default. The system automatically combines keyword precision with semantic intelligence, learning optimal weights from your customer behavior. You don’t configure the balance, it figures out what works best for each type of query.
From your perspective, it just means better search results. Customers find what they need faster, whether they search like a product catalog or like a human asking questions. The complexity happens behind the scenes; you just see improved sales and happier customers.
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Start Free TrialView PricingFrequently Asked Questions
Why use hybrid search instead of just semantic search?
How does hybrid search decide which technique to use?
Is hybrid search slower than regular search?
Can I control the balance between keyword and semantic search?
Related Terms
AI-Powered Search
Search technology that uses artificial intelligence and machine learning to understand queries, learn from behavior, and continuously improve results.
Keyword Search
Traditional search that matches exact words or phrases in product titles, descriptions, and attributes.
Semantic Search
Search technology that understands the meaning and context behind queries, rather than just matching keywords.
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