ElasticSearch: How to use edge_ngram and have real relevant hits to display first - elasticsearch

Question

I'm new with elasticsearch and I'm trying to develop a search for an ecommerce to suggested 5~10 matching products to the user.
As it should work while the user is typing, we found in the official documentation the use of edge_ngram and it KIND OF worked. But as we searched to test, the results were not the expected. As shows the example below (in our test)
Searching example
As it is shown in the image, the result for the term "Furadeira" (Power Drill) returns accessories before the power drill itself. How can I enhance the results? Even the order where the match is found in the string would help me, I guess.
So, this is the code I have until now:
//PUT example
{
"settings": {
"number_of_shards": 1,
"analysis": {
"filter": {
"autocomplete_filter": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 20
},
"portuguese_stop": {
"type": "stop",
"stopwords": "_portuguese_"
},
"portuguese_stemmer": {
"type": "stemmer",
"language": "light_portuguese"
}
},
"analyzer": {
"portuguese": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"portuguese_stop",
"portuguese_stemmer"
]
},
"autocomplete": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"autocomplete_filter"
]
}
}
}
}
}
/* mapping */
//PUT /example/products/_mapping
{
"products": {
"properties": {
"name": {
"type": "text",
"analyzer": "autocomplete",
"search_analyzer": "standard"
}
}
}
}
/* Search */
//GET /example/products/_search
{
"query" : {
"query_string": {
"query" : "furadeira",
"type" : "most_fields", // Tried without this aswell
"fields" : [
"name^8",
"model^10",
"manufacturer^4",
"description"
]
}
}
}
/* Product example */
// PUT example/products/38313
{
"name": "FITA VEDA FRESTA (ESPUMA 4503) 12X5 M [ H0000164055 ]",
"description": "Caracteristicas do produto:Ve…Diminui ruidos indesejaveis.",
"price":21.90,
"product_id": 38313,
"image": "http://placehold.it/200x200",
"quantity": 92,
"width": 20.200,
"height": 1.500,
"length": 21.500,
"weight": 0.082,
"model": "167083",
"manufacturer": "3M DO BRASIL"
}
Thanks in advance.

Answer 1

you could enhance your query to be a so-called boolean query, which contains your existing query in a must clause, but have an additional query in a should clause, that matches exactly (not using the ngrammed field). If the query matches the should clause it will be scored higher.
See the bool query documentation.

Answer 2

let's assume you have a field that differentiates the Main product from Accessories. I call it level_field.
now you can have two approaches to go:
1) boost up The Main product _score by adding 'should' operation:
put your main query in the must operation and in should operation use level_field to boost the _score of documents which are the Main products.
{
"query": {
"bool": {
"must": {
"match": {
"name": {
"query": "furadeira"
}
}
},
"should": [
{ "match": {
"level_field": {
"query": "level1",
"boost": 3
}
}},
{ "match": {
"level_field": {
"query": "level2",
"boost": 2
}
}}
]
}
}
}
2) in second approach you can decrease _score for documents that they are not the Main products by using boosting query:
{
"query": {
"boosting": {
"positive": {
"query_string": {
"query" : "furadeira",
"type" : "most_fields",
"fields" : [
"name^8",
"model^10",
"manufacturer^4",
"description"
]
}
}
},
"negative": {
"term": {
"level_field": {
"value": "level2"
}
}
},
"negative_boost": 0.2
}
}
}
I hope it helps