What is the most efficient way to boost a field with possible values ranging from 0 to 5000, scoring it according to its distribution?
Hi, For example, suppose the range of values has the following distribution 25th percentile: 100 50th percentile: 1000 75th percentile: 2000 100th percentile (max): 5000 Then, I want to sort them by score as follows 0 ~ 100: 1 point 100 ~ 1000: 2 points 1000 ~ 2000: 3 points 2000 ~ 5000: 4 points In this example, I've divided it into 4 parts, but in reality, I want to divide it into 100 parts and score them on a 100-point scoring scale. The current idea is to use the bf of eDisMax to force the score, and the bq to force the score. Also, although I haven't tried it yet, I think it would be faster to implement and use something like the staircase function, as it would reduce the number of function calls and make it easier to cache. I am trying to find out if it is possible to perform the above calculations on multiple fields and eventually add them together to achieve different searches for different individuals. Thanks. Translated with www.DeepL.com/Translator (free version)
