The concept of hybrid search is evolving and becoming increasingly available to companies to implement it on their websites.
Hybrid search means combining search technologies – but the technologies used to create hybrid search engines are changing.
That’s how it is today.
Traditionally, hybrid search has been understood as a combination of catalogue-based search engines and caterpillars.
Now this value is changing and means a combination of keyword search technology and artificial intelligence (AI).
AI replaces the complex understanding of traditional site search rules, and neural hashing makes vector search as fast as keyword search.
This may seem a bit complicated, but as a result users get a search without effort.
Today’s hybrid search technology makes it possible for queries such as “keep cool in the summer” on the website of the electronics store to instantly give out results on fans and air conditioners.
Without a hybrid search, the results may be restricted to products containing the words used in the query.
In addition to being faster and more relevant, hybrid searches are more available for implementation on their own websites.
Search.io is one of the organizations that are paving the way for a new future for hybrid search, and I recently had the opportunity to talk to CEO and co-founder Hamish Ogilvi.
He told me about the latest developments in this field and how the new tool of his company makes it easier for clients to search company websites. This, in turn, can lead to increased sales.
AI + neural hash = modern hybrid search
In the search, artificial intelligence replaces the strict matching of key words with dense vectors that contain the meaning of the text. It has proven to exceed key words in relevance, but slower delivery is a compromise.
Neural hashing, sometimes called “deep hashing”, allows vector searches to be as fast as keyword searches. It is named for its ability to use neural networks to hash vectors.
Neural hashes compare terms using mathematical expressions. They measure the difference between words and concepts and assign meaning to those closer to each other.
What does this mean in practice?
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Neural hashing gives 99% of the performance of a dense vector search, and it is more than 100 times faster and uses a fraction of the space”.
Ogilvy showed me several examples of queries in natural language with and without neural hashing to show how modern solutions fail retailers and buyers.
The “anything to keep the beer cold” request is the only irrelevant result on the Best Buy website. For comparison, the same query with neural hashes will return a page of results full of beer coolers.
This can play a crucial role between leaving a client empty-handed and selling quickly.
A hybrid search is more accessible to business than ever?
Search.io launches a tool called Neuralsearch, which is reported to combine the speed of traditional keyword search and the accuracy of vector search.
Simply put, it allows websites to deliver results, as Google does, without involving an army of search engineers.
This saves retailers the need to add synonyms to their search index, which takes a lot of time that can be spent on other purposes.