3.6 Elasticsearch-深度学习排序：Learning to Rank 插件安装与特征工程-洪萨配资

3.6 Elasticsearch-深度学习排序：Learning to Rank 插件安装与特征工程

3.6.1 为什么要在 Elasticsearch 里做 Learning to Rank

传统 TF-IDF、BM25 这类词袋评分函数在长尾查询、语义漂移、多字段混合场景下很快遇到天花板。把深度学习模型直接丢进离线打分再灌回 ES 虽然简单，但延迟高、实时性差。Elasticsearch Learning to Rank（LTR）插件把「特征抽取 → 模型推理 → 结果重排序」整条链路搬到 ES 内部，毫秒级响应，同时保留倒排索引的召回优势，是工业界「粗排+精排」架构的标配。

3.6.2 插件版本对齐矩阵

Elasticsearch	LTR 插件	JDK	Python 客户端
7.17.x	7.17.0.0	11	elasticsearch-ltr 1.3
8.11.x	8.11.0.0	17	elasticsearch-ltr 2.1

8.x 开始官方把transport-client完全移除，必须用 REST 低级客户端上传模型。

3.6.3 在线安装与集群滚动重启

每台 node 执行

sudobin/elasticsearch-plugininstall\https://github.com/o19s/elasticsearch-learning-to-rank/releases/download/v8.11.0.0/ltr-8.11.0.0.zip

校验

curl-XGET"localhost:9200/_ltr"|jq'.version'

滚动重启：每台 node 先/_cluster/nodes/_local/_shutdown等分片重分配完成再启下一台，保证绿色状态。

3.6.4 特征工程：从倒排到深度语义

LTR 把特征分成三类：

Query Feature：仅与查询相关，如查询长度、是否含品牌词。
Document Feature：仅与文档相关，如商品销量、发布时间。
Query-Document Feature：交叉特征，占模型 80% 以上权重。

下面给出电商搜索场景 18 维特征模板，可直接拷贝到ltr_feature_set.json：

{"name":"ecommerce_features","params":["keywords"],"feature":[{"name":"title_bm25","class":"org.apache.lucene.search.Explanation","query":{"match":{"title":"{{keywords}}"}}},{"name":"category_match","class":"org.apache.lucene.search.Explanation","query":{"term":{"category":"{{keywords}}"}}},{"name":"brand_exact","class":"org.apache.lucene.search.Explanation","query":{"term":{"brand.keyword":"{{keywords}}"}}},{"name":"sales","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"field_value_factor":{"field":"sales","modifier":"log1p"}}}},{"name":"price","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"field_value_factor":{"field":"price"}}}},{"name":"in_stock","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"filter":{"term":{"stock":true}},"weight":1}}},{"name":"discount","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"script_score":{"script":{"source":"Math.max(0.0, doc['marketPrice'].value - doc['price'].value) / doc['marketPrice'].value"}}}}},{"name":"title_length","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"script_score":{"script":{"source":"doc['title'].value.length()"}}}}},{"name":"query_length","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"script_score":{"script":{"source":"params._source.query.length()"}}}}},{"name":"title_embedding_dot","class":"org.apache.lucene.search.Explanation","query":{"script_score":{"script":{"source":"cosineSimilarity(params.queryVector, 'title_vector') + 1.0","params":{"queryVector":"{{query_vector}}"}}}}},{"name":"desc_embedding_dot","class":"org.apache.lucene.search.Explanation","query":{"script_score":{"script":{"source":"cosineSimilarity(params.queryVector, 'desc_vector') + 1.0","params":{"queryVector":"{{query_vector}}"}}}}},{"name":"recall_score","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"script_score":{"script":{"source":"_score"}}}}},{"name":"click_ctr","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"field_value_factor":{"field":"click_ctr","modifier":"log1p"}}}},{"name":"cart_ctr","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"field_value_factor":{"field":"cart_ctr","modifier":"log1p"}}}},{"name":"pay_ctr","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"field_value_factor":{"field":"pay_ctr","modifier":"log1p"}}}},{"name":"freshness","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"script_score":{"script":{"source":"Math.max(0, (params.now - doc['createTime'].value.getMillis()) / 86400000)","params":{"now":"{{now}}"}}}}}},{"name":"query_doc_jaccard","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"script_score":{"script":{"source":"double q = params.queryTerms.size(); double d = doc['title_terms'].size(); double i = 0; for (term in params.queryTerms) { if (doc['title_terms'].contains(term)) i++; } return i / (q + d - i + 1e-6);","params":{"queryTerms":"{{query_terms}}"}}}}}},{"name":"is_promotion","class":"org.apache.lucene.search.Explanation","query":{"function_score":{"filter":{"range":{"promotionStart":{"lte":"now"},"promotionEnd":{"gte":"now"}}},"weight":1}}}]}

上传命令：

curl-XPUT"localhost:9200/_ltr/_featureset/ecommerce_features"\-H'Content-Type: application/json'--data @ltr_feature_set.json

3.6.5 深度语义特征实时注入

title_embedding_dot依赖向量字段，需要在 mapping 里显式声明：

"title_vector":{"type":"dense_vector","dims":384,"similarity":"cosine"}

查询时把离线微调的 MiniLM 向量作为query_vector参数传进来即可，无需二次分词，延迟 <5 ms。

3.6.6 特征日志采样与存储

训练数据通过sltr查询生成，样例：

GET/products/_search{"query":{"match":{"title":"iphone 15"}},"rescore":{"window_size":100,"query":{"rescore_query":{"sltr":{"params":{"keywords":"iphone 15","query_vector":[...],"now":1700000000000},"featureset":"ecommerce_features","store":true,"logging":true}}}}}

ES 会把 18 维特征值写入.ltrstore索引，字段_ltrlog可直接拉下来做 LibSVM 格式转换：

curl-XGET"localhost:9200/.ltrstore/_search?q=_ltrlog:*"\|jq -r'.hits.hits[]._source._ltrlog'>train.svmlight

3.6.7 模型训练与上传

XGBoost 示例：

importxgboostasxgb dtrain=xgb.DMatrix('train.svmlight')params={'objective':'rank:pairwise','eta':0.1,'max_depth':6}bst=xgb.train(params,dtrain,num_boost_round=300)bst.save_model('xgb_model.json')

上传：

curl-XPUT"localhost:9200/_ltr/_model/xgb_model"\-H'Content-Type: application/json'\--data-binary @xgb_model.json

3.6.8 线上 A/B：粗排 + LTR 精排

"rescore":{"window_size":200,"query":{"rescore_query":{"sltr":{"model":"xgb_model","params":{"keywords":"{{keywords}}","query_vector":"{{query_vector}}","now":"{{now}}"}}},"query_weight":0,"rescore_query_weight":1}}