Elasticsearch: Multiple partial words not scored high enough - elasticsearch

Question

so I'm trying to get good search results out of an Elasticsearch installation.
But I run into problems when I'm trying to make a fuzzy search on some very simple data.
Somehow multiple (some of them partial) words are scored too low and only get scored higher, when more letters of the word are present in the search query.
Let me explain:
I have a simple index built with two simple documents.
{
"name": "Product with good qualities and awesome sound system"
},
{
"name": "Another Product that has better acustics than the other one"
}
Now I query the index with this parameters:
{
"query": {
"multi_match": {
"fields": ["name"],
"query": "product acust",
"fuzziness": "auto"
}
}
}
And the results look like this:
"hits": [
{
"_index": "test_products",
"_type": "_doc",
"_id": "1",
"_score": 0.19100355,
"_source": {
"name": "Product with good qualities and awesome sound system"
}
},
{
"_index": "test_products",
"_type": "_doc",
"_id": "2",
"_score": 0.17439455,
"_source": {
"name": "Another Product that has better acustics than the other one"
}
}
]
As you can see the product with the ID 2 is scored less than the other product even though it has possibly more similarity with the given query string than the other product because it has 1 full word match and 1 partial word match.
When the query would looke like "product acusti" the results would start to behave correctly.
I've already fiddled around with bool search but the results are identical.
Any ideas how I can get the wanted results back faster than having to have almost the whole second word typed in?

Answer 1

As far as I know, Elasticsearch does not do partial word matching by default, so the term acust is not matched in neither of your documents.
The reason you are getting a higher score in the first document is that your matched term, product, appears in a shorter sentence:
Product with good qualities and awesome sound system
But as for the second document, product appears in a longer sentence:
Another Product that has better acoustics than the other one
So your second document is getting a lower score because the ratio of your match term (product) to the number of terms in the sentence is lower.
In other words in has lower Field length normalization:
norm = 1/sqrt(numFieldTerms)
Now if you you want to be able to do partial prefix matching, you need to tokenize your term into ngrams, for example you can create the following ngrams for the term "acoustics":
"ac", "aco", "acou", "acous", "acoust", "acousti", "acoustic", "acoustics"
You have 2 options to achieve this, see the answer by Russ Cam on this question
use Analyze API
with an analyzer that will tokenize the field into tokens/terms from
which you would want to partial prefix match, and index this
collection as the input to the completion field. The Standard analyzer
may be a good one to start with...
Don't use the Completion Suggester here and instead set up your field (name) as a text datatype with
multi-fields
that include the different ways that name should be analyzed (or not
analyzed, with a keyword sub field for example). Spend some time with the Analyze API to build an analyzer that will
allow for partial prefix of terms anywhere in the name. As a start,
something like the Standard tokenizer, Lowercase token filter,
Edgengram token filter and possibly Stop token filter would get you
running...
You may also find this guide helpful.