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Do you ever get frustrated searching for the wanted product on the e-commerce store? Do you type in a keyword or phrase, but the search engine won’t give you the results you’re looking for?

The answer might be a fuzzy search.

In this blog post, we will explore the mystery of fuzzy search and how you can use it to your advantage.

Fuzzy search, also known as approximate string matching, is a process of finding strings that approximate a given pattern.

It is a type of search that allows users to find relevant results even if they don’t use the exact terms or spellings. It means users don’t have to worry about spelling mistakes or typos when searching for something.

The fuzzy search algorithm works by looking for patterns in strings rather than exact matches. It considers the context of the words and looks for similar words or phrases that match the original query.

For example, if you search for “dog”, the fuzzy search algorithm will also look for words such as “puppy”, “canine”, and “hound”.

Fuzzy search is becoming increasingly popular as it makes the search easier and more accurate. It is especially useful in e-commerce.

When used with Natural Language Processing capabilities, fuzzy search helps the e-commerce search algorithm get to the core of users’ search queries and deliver only user-relevant search results.

How Does Fuzzy Search Work?

The fuzzy search algorithm uses a set of rules to compare strings and identify patterns. For example, the Levenshtein Distance algorithm considers the minimum number of edits needed between strings, which is crucial for its effectiveness [1].

To do this, it looks at the similarities between strings, such as the number of characters, the order of characters, and the distance between characters. It then uses these similarities to find matches.

For example, in LupaSearch e-commerce search, the AI-driven fuzzy search algorithm would understand the relationship between words: that “orange” and “apple” (although different objects) are more related than “orange” and “desk.”

Here’s how it works.

Fuzzy Search Algorithms

The most common algorithms are the Levenshtein Distance algorithm and the Soundex algorithm. However, other algorithms can also be used, such as the Damerau-Levenshtein algorithm and the Jaccard index.

The Levenshtein Distance algorithm is the most commonly used fuzzy search algorithm. It is well-regarded for its ability to determine how similar two strings are by counting operations needed to transform one string into the other [2].

It looks at the number of characters that are different between two strings and calculates the “edit distance” between them. If the edit distance is below a certain threshold, then the strings are considered to be a match.

The Soundex algorithm looks at the phonetic similarities between two strings. It takes into account the pronunciation of words and looks for similar sounds. For example, if you search for “dog”, the Soundex algorithm will also look for words such as “dawg” and “dock”.

The Damerau-Levenshtein algorithm is an improved version of the Levenshtein Distance algorithm. It takes into account the number of characters that are different between two strings, as well as the number of characters that are transposed.

Fuzzy search has many benefits, such as improved accuracy and faster search results. Research supports that fuzzy search algorithms significantly increase search accuracy by allowing spelling variances, thus reducing the requirement for precise input [3].

It can help users find relevant results even if they don’t enter the exact terms. It is especially useful when they’re unsure of the exact spelling or phrase.

Fuzzy search also makes it easier to search for misspelled words or typos. It is particularly useful for online search engines that want to provide accurate results for users. It can also reduce the time it takes to find a particular result.

Examples of Fuzzy Search Applications

Fuzzy search is used in a variety of applications, from e-commerce search engines to customer relationship management (CRM) systems.

Here are some examples of how it is used:

  • E-commerce search engines: Fuzzy search algorithms are used by e-commerce search engines such as LupaSearch to provide users with more accurate search results and a satisfying shopping experience.
  • Social media: Fuzzy search algorithms are used by social media platforms such as Twitter and Facebook to allow users to find relevant posts and accounts.
  • CRM systems: Fuzzy search algorithms are used by customer relationship management (CRM) systems to enable users to quickly find customer information.
  • Voice search: Fuzzy search algorithms are used by voice search systems such as Alexa and Siri to enable users to better find information.

If you want to make the most of a fuzzy search, there are some strategies that you can exploit.

Here are some tips for optimizing your fuzzy search results:

  • Use the most relevant keywords: Try to use the most accurate keywords when searching. It will help the fuzzy search algorithm to identify the most relevant results.
  • Include synonyms: Include synonyms in your search query. It will help the fuzzy search algorithm to identify related terms that may be useful.
  • Use wildcards: Wildcards can be used to match any character. It can help to broaden your search and find more relevant results.
  • Avoid typos: Avoid typos and spelling mistakes in your search query. It will help the fuzzy search algorithm to identify the most accurate results.

There are some challenges associated with fuzzy search. The most common challenge is that fuzzy search algorithms can produce too many false positives. It means that the algorithm will return results that are not relevant to the query.

Another challenge is that fuzzy search algorithms can be slow. This is because they have to compare a large number of strings to find matches. This can be especially problematic when dealing with large datasets.

Conclusion

A fuzzy search is a powerful tool that can help you find relevant results even if you do not use the exact terms. It is becoming increasingly popular, especially in e-commerce search, as it makes searches easier and more accurate.

If you want to make the most out of fuzzy search and uplift your e-commerce search experience, exploit an AI-driven e-commerce search provider such as LupaSearch.

LupaSearch uses NLP, machine learning, and fuzzy search algorithms to understand user query intents and improve search results’ relevance over time.

LupaSearch offers an intuitive dashboard so that even non-tech-savvy shop administrators can analyze the effectiveness of search results.

The best thing is that you do not need extensive IT knowledge. The LupaSearch team supports you and helps you improve search relevance, simultaneously growing your business revenue.

Contact us today, and let’s have a productive conversation.

References

  1. Heikki Hyyrö (2005). Bit-parallel approximate string matching algorithms with transposition. Journal of Discrete Algorithms, Volume 3, Issues 2–4, June 2005, Pages 215-229. https://doi.org/10.1016/j.jda.2004.08.006

  2. Zhao, C., & Sahni, S. (2018). Linear space string correction algorithm using the Damerau-Levenshtein distance. BMC Bioinformatics, 21. https://doi.org/10.1186/s12859-019-3184-8

  3. Boguszewski, A., Szymański, J., & Draszawka, K. (2016). Towards increasing F-measure of approximate string matching in O(1) complexity. 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), 527-532. https://doi.org/10.15439/2016F311