Is it possible to normalize the score before the boosts gets applied?
Let's say I have 2 documents
Doc1:
text: xxxx
description: xxxx
number_published_in_this_year: 1
Doc2:
text: xxxx
description: xxxx
number_published_in_this_year: 10
Now when I search, assume if q=cookie&qf=title^10 description^10&bf=number_published_in_this_year^5
Assume tf-idf scores are as below:
Doc1: title - 4 description - 5
Doc2: title - 2.5 description - 1.5
With the normal approach, final score calculation would be
Final scores:
Doc1: 4*10 + 5*10 + 1*5 = 95
Doc2: 2.5*10 + 1.5*10 + 10*5 = 90
The idea is to normalize the score, so that text matching scores will not dominate other factors. ( in this case, number_published_in_this_year is much larger in case of the second document)
Doc1: 4/5 *10 + 5/5 *10 + 1*5 = 23
Doc2: 2.5/5 *10 + 1.5/5 *10 + 10*5 = 58
(or)
Doc1: 90/90 + 1*5 = 6
Doc2: 40/90 + 10*5 = 50.4
Now since doc2 has higher score, it will come on top.
Is this possible? Can someone help me on this?