导语

比如用户索索“住宿”，但是没有一家酒店的名字中包含住宿，然后给用户返回了一个空白页。除了给“住宿”添加同义词“酒店”外 ，这个问题还可以这样解决：为每一个 catetory_id 维护一组关键词，这组关系维护在一个 Map 中，当一个 keyword 进入 Service 层后，先 Analyze 它，得到的 token 去 Map 中查找对应的 category_id，然后可以将这个 category_id 放在 Query 里影响召回，也可以放在 filter 中影响排序。一般的做法是：先影响排序，如果得不到结果，再影响召回；

---

category_id 和关键词的映射

// category_id 下的所有关键词
    private Map<Integer,List<String>> categoryWorkMap = new HashMap<>();

@PostConstruct
public void init(){
    categoryWorkMap.put(1,new ArrayList<>());
    categoryWorkMap.put(2,new ArrayList<>());

    categoryWorkMap.get(1).add("吃饭");
    categoryWorkMap.get(1).add("下午茶");

    categoryWorkMap.get(2).add("休息");
    categoryWorkMap.get(2).add("睡觉");
    categoryWorkMap.get(2).add("住宿");
}

查询 keyword 对应的 category_id

/**
 * GET /shop/_analyze
 * {
 *   "field": "name",
 *   "text": "凯悦"
 * }
 * 先分词 Keyword，再看分词后的每个 token 是否对应了某个 category_id，最后将每个 token 对应哪个 category_id 返回出来；
 * @param keyword
 * @return
 * @throws IOException
 */
private Map<String,Object> analyzeCategoryKeyword(String keyword) throws IOException {
    Map<String,Object> res = new HashMap<>();

    Request request = new Request("GET","/shop/_analyze");
    request.setJsonEntity("{" + "  \"field\": \"name\"," + "  \"text\":\"" + keyword + "\"\n" + "}");
    Response response = highLevelClient.getLowLevelClient().performRequest(request);
    String responseStr = EntityUtils.toString(response.getEntity());
    JSONObject jsonObject = JSONObject.parseObject(responseStr);
    JSONArray jsonArray = jsonObject.getJSONArray("tokens");
    for(int i = 0; i < jsonArray.size(); i++){
        String token = jsonArray.getJSONObject(i).getString("token");
        Integer categoryId = getCategoryIdByToken(token);
        if(categoryId != null){
            res.put(token, categoryId);
        }
    }

    return res;
}

private Integer getCategoryIdByToken(String token){
    for(Integer key : categoryWorkMap.keySet()){
        List<String> tokenList = categoryWorkMap.get(key);
        if(tokenList.contains(token)){
            return key;
        }
    }
    return null;
}

影响召回的 Query DSL

• 使用 bool 查询的 should 子句；

GET /shop/_search 
{
  "_source": "*",
  "script_fields": {
    "distance": {
      "script": {
        "source": "haversin(lat,lon,doc['location'].lat,doc['location'].lon)",
        "lang": "expression",
        "params": {"lat":31.23916171,"lon":121.48789949}
      }
    }
  },
  "query": {
    "function_score": {
      "query": {
        "bool": {
          "must": [
            {
              "bool": {
                "should": [
                  {"match": {"name": {"query": "住宿","boost": 0.1}}},
                  {"term":{"category_id":2}}
                ]
              }
            },
            {"term": {"seller_disabled_flag": 0}}
          ]
        }
      },
      "functions": [
        {
          "gauss": {
            "location": {
              "origin": "31.23916171,121.48789949",
              "scale": "100km",
              "offset": "0km",
              "decay": 0.5
            }
          },
          "weight": 9
        },
        {
          "field_value_factor": {
            "field": "remark_score"
          },
          "weight": 0.2
        },
        {
          "field_value_factor": {
            "field": "seller_remark_score"
          },
          "weight": 0.1
        }
      ],
      "score_mode": "sum",
      "boost_mode": "sum"
    }
  },
  "sort": [ 
    {
      "_score": {
        "order":"desc"
      }
    }
  ]
}

影响排序的 Query DSL

• 在 Function Query 的 Function 中添加一个 filer:

GET /shop/_search 
  {
    "_source": "*",
    "script_fields": {
      "distance": {
        "script": {
          "source": "haversin(lat,lon,doc['location'].lat,doc['location'].lon)",
          "lang": "expression",
          "params": {"lat":31.23916171,"lon":121.48789949}
        }
      }
    },
    "query": {
      "function_score": {
        "query": {
          "bool": {
            "must": [
              {
                "bool": {
                  "should": [
                    {"match": {"name": {"query": "住宿","boost": 0.1}}}
                  ]
                }
              },
              {"term": {"seller_disabled_flag": 0}}
            ]
          }
        },
        "functions": [
          {
            "gauss": {
              "location": {
                "origin": "31.23916171,121.48789949",
                "scale": "100km",
                "offset": "0km",
                "decay": 0.5
              }
            },
            "weight": 9
          },
          {
            "field_value_factor": {
              "field": "remark_score"
            },
            "weight": 0.2
          },
          {
            "field_value_factor": {
              "field": "seller_remark_score"
            },
            "weight": 0.1
          },
          {
            "filter": {"term":{"category_id": 2}},
            "weight": 0.2
          }
        ],
        "score_mode": "sum",
        "boost_mode": "sum"
      }
    },
    "sort": [ 
      {
        "_score": {
          "order":"desc"
        }
      }
    ]
  }