This plugin allows you to score documents based on arbitrary raw vectors, using dot product or cosine similarity.
The aim of this plugin is to enable real-time scoring of vector-based models, in particular factor-based recommendation models.
In this case, user and item factor vectors are indexed using
the Delimited Payload Token Filter,
e.g. the vector [1.2, 0.1, 0.4, -0.2, 0.3] is indexed as a string:
0|1.2 1|0.1 2|0.4 3|-0.2 4|0.3.
This stores the vector indices as "terms" and the vector values as "payloads".
This plugin provides a native script payload_vector_score for use
in function_score queries.
The script computes the dot product between the query vector and the document vector. In pseudo-code:
for (i : vector_indices_terms) {
payload = indexTermField(i).getPayload()
score += payload * queryVector(i)
}Targets Elasticsearch 2.4.1 and Java 1.8.
- Build:
mvn package - Install plugin in Elasticsearch:
ELASTIC_HOME/bin/plugin install file:///PROJECT_HOME/target/releases/elasticsearch-vector-scoring-2.4.1.zip(stop ES first). - Start Elasticsearch:
ELASTIC_HOME/bin/elasticsearch
You should see the plugin registered at Elasticsearch startup:
[2016-10-21 14:07:59,596][INFO ][plugins ] [Starstreak] modules [reindex, lang-expression, lang-groovy], plugins [elasticsearch-vector-scoring], sites []
curl -s -XPUT 'http://localhost:9200/test?pretty' -d '{
"settings" : {
"analysis": {
"analyzer": {
"payload_analyzer": {
"type": "custom",
"tokenizer":"whitespace",
"filter":"delimited_payload_filter"
}
}
}
}
}'
curl -s -XPUT 'http://localhost:9200/test/_mapping/movies?pretty' -d '
{
"movies" : {
"properties" : {
"@model_factor": {
"type": "string",
"term_vector": "with_positions_offsets_payloads",
"analyzer" : "payload_analyzer"
}
}
}
}'
curl -s -XPUT 'http://localhost:9200/test/movies/1?pretty' -d '
{
"@model_factor":"0|1.2 1|0.1 2|0.4 3|-0.2 4|0.3",
"name": "Test 1"
}'
curl -s -XPUT 'http://localhost:9200/test/movies/2?pretty' -d '
{
"@model_factor":"0|0.1 1|2.3 2|-1.6 3|0.7 4|-1.3",
"name": "Test 2"
}'
curl -s -XPUT 'http://localhost:9200/test/movies/3?pretty' -d '
{
"@model_factor":"0|-0.5 1|1.6 2|1.1 3|0.9 4|0.7",
"name": "Test 3"
}'
curl -s -XGET 'http://localhost:9200/test/movies/1/_termvector?pretty' -d '
{
"fields" : ["@model_factor"],
"payloads" : true,
"positions" : true
}'curl -s -XPOST 'http://localhost:9200/test/movies/_search?pretty' -d '
{
"query": {
"function_score": {
"query" : {
"query_string": {
"query": "*"
}
},
"script_score": {
"script": "payload_vector_score",
"lang": "native",
"params": {
"field": "@model_factor",
"vector": [0.1,2.3,-1.6,0.7,-1.3],
"cosine" : true
}
},
"boost_mode": "replace"
}
}
}'This query returns results sorted by cosine similarity (including the document itself). For "similar item" style recommendations, you can filter the query item from the returned results.
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 3,
"max_score" : 0.99999994,
"hits" : [ {
"_index" : "test",
"_type" : "movies",
"_id" : "2",
"_score" : 0.99999994,
"_source" : {
"@model_factor" : "0|0.1 1|2.3 2|-1.6 3|0.7 4|-1.3",
"name" : "Test 2"
}
}, {
"_index" : "test",
"_type" : "movies",
"_id" : "3",
"_score" : 0.2175577,
"_source" : {
"@model_factor" : "0|-0.5 1|1.6 2|1.1 3|0.9 4|0.7",
"name" : "Test 3"
}
}, {
"_index" : "test",
"_type" : "movies",
"_id" : "1",
"_score" : -0.19618797,
"_source" : {
"@model_factor" : "0|1.2 1|0.1 2|0.4 3|-0.2 4|0.3",
"name" : "Test 1"
}
} ]
}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent