我有一个关键字列表与相关分数，我想用它来匹配的elasticsearch索引文件。目标是通过输入关键字来匹配文档，并通过输入分数和文档关键字分数的组合来进行排名。
搜索输入示例：

[{
    "name": "golang",
    "score": 0.2
}, {
    "name": "c++",
    "score": 0.13
}, {
    "name": "php",
    "score": 0.07
}]

两个示例文档：

[{
    "name": "John",
    "location": "USA",
    "scoredKeywords": [
        {
            "name": "php",
            "score": 0.7
        }, {
            "name": "golang",
            "score": 0.3
        }, {
            "name": "java",
            "score": 0.17
        }]
}, {
    "name": "Bob",
    "location": "USA",
    "scoredKeywords": [
        {
            "name": "javascript",
            "score": 0.8
        }, {
            "name": "php",
            "score": 0.01
        }, {
            "name": "golang",
            "score": 0.01
        }]
}, {
    "name": "Dave",
    "location": "USA",
    "scoredKeywords": [
        {
            "name": "golang",
            "score": 0.9
        }, {
            "name": "html",
            "score": 0.4
        }, {
            "name": "css",
            "score": 0.3
        }]
}]

我可以根据需要灵活地进行Map。
我对排名的想法是这样的：

Dave: (overall: 1800)
    golang: 0.9
    rank: (0.9 * 100) * (0.2 * 100) = 1800
    html: 0.4
    rank: n/a
    css: 0.3
    rank: n/a
John: (overall: 490 + 600 = 1090)
    php: 0.7
    rank: (0.7*100) * (0.07*100) = 490
    golang: 0.3
    rank: (0.3*100) * (0.2 * 100) = 600
    java: 0.17
    rank: n/a
Bob: (overall: 7 + 20 = 27)
    javascript: 0.8
    rank: n/a
    php: 0.01
    rank: (0.01 * 100) * (0.07 * 100) = 7
    golang: 0.01
    rank: (0.01 * 100) * (0.2 * 100) = 20