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Raoul Meyer on February 09, 2019

People are bound to make typo's. You probably know how Google handles these situations: The search engine suggests a corrected search term, or ev... [Read Full]
 

Every single time I used this, I kinda regretted. It was hard to configure for bigger datasets and I often was getting funny suggestions.

For instance beer would be suggested as bear 😂

 

Yeah agreed, it can give interesting suggestions. That's why I would mostly use it in cases where any alternative is better than showing no results.

Out of curiosity, what was your use case? Did you try limiting your suggestions with a collate query?

 

To solve the problem in this example ("bear" vs "beer") - we need to provide user's query history alongside the current request. So search can determine if user hunts often, or uses Bear framework, or cooks wheat beer

That's a very good suggestion. Thank you.

 

It was classic search. Search data consisted of categories, countries, companies, brands, other attributes. Millions of possible combinations of these.

I did use collate query but it was very likely that suggestion with bear also would have brought back results. But there's a chance I did something wrong. 🤔

 

With these things I always wonder how much of this is pure ElasticSearch or "just" some icing on Lucene.

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