Best way to trim results by score?

Elastic Stack Elasticsearch
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Some of my search results returns a total of over 10k documents, varying from a high score (in my most recent search, ~75) to a very low score (less than 5). Other queries return a high score of ~20 and a low score of ~1.

Does anyone have a good solution for trimming off the less relevant documents? A java or query implementation would work. I've thought about using min_score, but i'm wary of that since it has to be a constant number, and some of the scores of my responses are a lot closer than the above. I suppose I could come up with some formula based off of the returned scores to create a cutoff for every response, but I was curious if anyone has come up with a solution to a similar use case?

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Hi Jon,

In general it is recommended to not do anything like that and just return documents in descending order of score so that the most relevant ones appear first.

Instead of using a score cutoff, the general approach is usually to use a cutoff on the rank and a rescorer. For instance you could take the 10 best documents by relevance and reorder them based on some other criteria that denotes the authority or popularity of the document: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-rescore.html

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If you're building a faceted search interface using aggregations it's often useful to do this to avoid a long-tail making a nonsense of your facet summaries. Someone searching for a video by typing ice age shouldn't be told there's 300 matches in the electricals department just because you matched a lot of refrigerators with an ice dispenser.
One technique I've seen used in e-commerce sites is to start with a very tight interpretation of user input e.g. running the input ice age as a strict "ice age" phrase match. Only if the results are very few in number do they re-run a relaxed form of the search i.e. ice OR age. Obviously picking what that magic threshold number is can be tricky and offering users ways to rewrite the query can help.

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