Fuzzy search finds similar words and is very useful in terms of handling spelling errors made while searching on a website.
How fuzzy search works?
Fuzzy search works by using mathematical formulae that calculate the distance (or similarity between ) two words. One such commonly used method is called the Levenshtein distance.
Mathematically, the formula is (which we will not discuss in this article)
Here we will use certain examples to illustrate this-
For example, when you do a search for fitbt in expertrec’s custom search, these are the results we get which show the fuzzy search at work.
As you can see, the first result is fitbit.
Now let’s calculate the levenshtein distance between the words w1=fitbt and w2=fitbit
Levenshtein distance =1
Now to understand why fitness doesn’t come for the search query fitbt, let’s calculate the levenshtein distance between the words w1=fitbt and w2=fitness.
As you can see in the image below, levenshtein distance =4 
When the levenshtein distance is more, the words are more dissimilar and come lower in search results.
Create your own fuzzy search engine here.
FAQs
How to implement fuzzy search?
A fuzzy search looks for text that closely rather than precisely matches a keyword. Even when
your search parameters are mistyped, fuzzy searches can still help you identify relevant
results. Put a tilde (~) at the end of the search word to conduct a fuzzy search.
A fuzzy matching algorithm gets used to executing a fuzzy search, producing a list of
outcomes based on potential relevancy even when the words and spellings in the search input
may not precisely match. Exact and highly relevant matches appear at the top of web search lists. Ratings of subjective significance may be provided, often as percentages.
How to use fuzzy searching?
Deduplication is one of the most often used applications of Fuzz Search, and it has a wide
range of use cases. Imagine constantly displaying the same digital advertisement to a person
who has previously responded favorably to one and adversely to another. What would happen
to the user experience if a financial institution required fraud detection for a transaction the
customer performed every week? The usage of approximate string matching has made
deduplication possible for record streamlining in many modern data
systems.
When used for inquiry and investigation, fuzzy searching is far more effective than accurate
searching. It is beneficial when looking up new, complex phrases in a foreign
language for which the correct spellings aren’t commonly recognized. Fuzzy searching may also
find people using little or imperfect identifying information.
Why Do Businesses Use Fuzzy Matching?
This capacity is provided by fuzzy matching. It is because it can do so across several data sources,
it aids semantic search by raising the threshold of the entity match. The internal data of a
company, customer data, sales figures, customer profiling, medical information, and other
business applications depend on this.
Here are some explanations as to why companies employ it –
- For a unified customer view, combine customer records.
- Deduplicate data and eliminate it.
- Data preparation and cleaning before analysis.
- Standardize data for improved insight accuracy.
- Detection of fraud.
- Enhance and combine data from many sources.
- For segmentation, create client profiles.
- Compare information for permits and compliance.
Data analytics must produce highly accurate findings, whether they are used for customer review assessment, social video content assessment, or any other company function. Despite its complexity and fuzzy matching can be helpful in this process.
