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. 🤔
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
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?
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. 🤔
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.