What is Hybrid Search?
Giving users the ability to search for and find something on your website is a crucial part of ensuring a good user experience. Developers have achieved this in the past by using different search technologies such as crawler-based search engines like Google and directory-based search engines. This has changed with the rise of hybrid search. In this article, we will have a look at what hybrid search is and how you can use it on your website.
Hybrid Search: An Overview
As the name suggests, hybrid search is the use of two or more search technologies on the same platform, whether it be on a website or elsewhere. In the modern age, the most popular combination is the use of keyword-based search technologies and AI (artificial intelligence).
In the past, and on many websites and platforms not using hybrid search, on-site search depends on a complex set of rules. These rules helped to determine the content or results that were closest to the keyword you entered.
AI has replaced this type of search, with the use of natural hashing making vector-based searches as fast as those done using keywords.
Understanding The Jargon
In modern hybrid search, we use AI to replace keyword matching with vectors. This means instead of trying to match the keywords to content to give you results, vectors are used instead.
Vector-based search is a type of search that uses machine learning to match the meaning of the keyword you have entered to other content which can be text, audio, or images.
For example, a website that uses modern hybrid search can take in the keyword “keep cool” and show you results that include air conditioners and fans. Without this technology, only results that contain this exact keyword would be shown.
AI using dense vectors for search is much better for finding relevant results, but it is often slower than keyword-based search.
Neural hashing, on the other hand, is a technology that uses neural networks to hash different vectors. The neural hashes used compare different terms and measure differences between concepts and words. They then assign meaning to those that are related.
This means a neural network using neural hashing can take the words “dog collar” and match them to an image (vector) of a dog wearing a collar. Neural hashing makes a vector-based search much faster, typically as fast as keyword-based searching.
Hybrid Search is Becoming More Accessible
Several companies are launching or are close to launching hybrid search services and tools that have the accuracy of vector-based search and the speed of keyword search. Once deployed, these tools and services will make searching on websites as fast as a search done on Google.
By using these services, businesses do not have to add synonyms to their search indexes because neural hashing takes care of this for them. This saves a lot of time and money for a business.
The above is done without the intervention of additional engineers.
Hybrid search is the future for any business that wants to implement fast searches that lead to accurate and relevant search results. It can be incredibly useful, particularly for e-commerce websites and those with lots of data users need to sift through before finding what they are looking for.