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elasticsearch-feature-vector-scoring

★ 69 Open GitHub ↗

A simple scoring plugin for vector in Elasticsearch.

// repository documentation

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## Use Case
You can use this plugin to calculate the relevance score of the two feature vector, such as:
- Personalized Search;
- Find similar product;
- Product recommendation;

## Build Plugin from Code
1. git clone https://github.com/ginobefun/elasticsearch-feature-vector-scoring.git
2. mvn clean package -DskipTests
3. create vector scoring plugin dircectoy, such as ${ES_HOME}/plugins/vector-scoring
4. copy target/releases/elasticsearch-feature-vector-scoring-5.3.0.zip to your plugin directory and unzip it
5. restart elasticsearch

## Script Parameters
- **field**: **required**, field in index to store the vector of document;
- **inputFeatureVector**: **required**,  the condition vector, a string connected with comma;
- **version**: version of vector, if it isn't null, it should match the version of vector of document(if use version, the field value should start with ‘$VERSION|’, such as '20170331|0.1,2.3,-1.6,0.7,-1.3');
- **baseConstant** and **factorConstant**: used to calculate the final score, default value are 1. final_score = baseConstant + factorConstant * cos(X, Y)

## Example
### create a test index

PUT /test
    {
      "mappings": {
        "product": {
          "properties": {
            "productName": {
              "type": "text",
              "analyzer": "standard"
            },
            "productFeatureVector": {
              "type": "keyword"
            }
          }
        }
      },
      "settings": {
        "index": {
          "number_of_shards": 1,
          "number_of_replicas": 0
        }
      }
    }

### index some documents

POST /test/product/1
    {
      "productName": "My favorite brand of shoes",
      "productFeatureVector": "0.1,2.3,-1.6,0.7,-1.3"
    }
    
    POST /test/product/2
    {
      "productName": "Normal brand of shoes",
      "productFeatureVector": "-0.5,1.6,1.1,0.9,0.7"
    }
    
    POST /test/product/3
    {
      "productName": "The shoes I don't like",
      "productFeatureVector": "1.2,0.1,0.4,-0.2,0.3"
    }

### normal search

POST /test/_search
    {
      "query": {
        "match": {
          "productName": "shoes"
        }
      }
    }

the the result is:

{
      "took": 8,
      "timed_out": false,
      "_shards": {
        "total": 1,
        "successful": 1,
        "failed": 0
      },
      "hits": {
        "total": 3,
        "max_score": 0.14181954,
        "hits": [
          {
            "_index": "test",
            "_type": "product",
            "_id": "2",
            "_score": 0.14181954,
            "_source": {
              "productName": "Normal brand of shoes",
              "productFeatureVector": "-0.5,1.6,1.1,0.9,0.7"
            }
          },
          {
            "_index": "test",
            "_type": "product",
            "_id": "1",
            "_score": 0.1273061,
            "_source": {
              "productName": "My favorite brand of shoes",
              "productFeatureVector": "0.1,2.3,-1.6,0.7,-1.3"
            }
          },
          {
            "_index": "test",
            "_type": "product",
            "_id": "3",
            "_score": 0.1273061,
            "_source": {
              "productName": "The shoes I don't like",
              "productFeatureVector": "1.2,0.1,0.4,-0.2,0.3"
            }
          }
        ]
      }
    }

### search with feature vector score

POST /test/_search
    {
      "query": {
        "function_score": {
          "query": {
            "match": {
              "productName": "shoes"
            }
          },
          "functions": [
            {
              "script_score": {
                "script": {
                  "inline": "feature_vector_scoring_script",
                  "lang": "native",
                  "params": {
                    "inputFeatureVector": "0.1,2.3,-1.6,0.7,-1.3",
                    "field": "productFeatureVector"
                  }
                }
              }
            }
          ],
          "boost_mode": "multiply"
        }
      }
    }

and the result is:

{
      "took": 35,
      "timed_out": false,
      "_shards": {
        "total": 1,
        "successful": 1,
        "failed": 0
      },
      "hits": {
        "total": 3,
        "max_score": 0.2546122,
        "hits": [
          {
            "_index": "test",
            "_type": "product",
            "_id": "1",
            "_score": 0.2546122,
            "_source": {
              "productName": "My favorite brand of shoes",
              "productFeatureVector": "0.1,2.3,-1.6,0.7,-1.3"
            }
          },
          {
            "_index": "test",
            "_type": "product",
            "_id": "2",
            "_score": 0.17267346,
            "_source": {
              "productName": "Normal brand of shoes",
              "productFeatureVector": "-0.5,1.6,1.1,0.9,0.7"
            }
          },
          {
            "_index": "test",
            "_type": "product",
            "_id": "3",
            "_score": 0.10233018,
            "_source": {
              "productName": "The shoes I don't like",
              "productFeatureVector": "1.2,0.1,0.4,-0.2,0.3"
            }
          }
        ]
      }
    }
    
### A personalized search case details
see http://ginobefunny.com/post/personalized_search_implemention_based_word2vec_and_elasticsearch/

elasticsearch-feature-vector-scoring

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