向量代码兼容

@@ -28,7 +28,7 @@ public class RecommendBrandAggregation extends AbstractAggregation {
 		this.simpleFieldAggs = new ArrayList<SimpleFieldAgg>();
 		simpleFieldAggs.add(new SimpleFieldAgg(aggName(), ProductIndexEsField.brandId, 300));
 		// 构造TopHitOrder
-		if (personalVectorFeatureSearch.getPersonalizedSearch(paramMap) != null) {
+		if (personalVectorFeatureSearch.queryPersonalizedSearch(paramMap) != null) {
 			topHitOrder = "_score:desc";
 		}
 	}

@@ -36,7 +36,7 @@ public class RecommendShopAggregation extends AbstractAggregation {
 		this.simpleFieldAggs = new ArrayList<SimpleFieldAgg>();
 		simpleFieldAggs.add(new SimpleFieldAgg(aggName(), ProductIndexEsField.shopId, shopCount));
 		// 构造topHit排序规则
-		if (personalVectorFeatureSearch.getPersonalizedSearch(paramMap) != null) {
+		if (personalVectorFeatureSearch.queryPersonalizedSearch(paramMap) != null) {
 			topHitOrder = "_score:desc";
 		}
 	}

@@ -6,7 +6,9 @@ import java.util.List;
 import java.util.Map;
 import java.util.stream.Collectors;
 
+import com.yoho.search.base.utils.ConvertUtils;
 import com.yoho.search.service.recall.config.SpecialShopConstants;
+import org.apache.commons.collections.CollectionUtils;
 import org.apache.commons.collections.MapUtils;
 import org.apache.commons.lang.StringUtils;
 import org.elasticsearch.common.lucene.search.function.CombineFunction;
@@ -74,7 +76,7 @@ public class FunctionScoreSearchHelper {
         // 3、构造functionScoreQueryBuilder
         YohoFilterFunctionBuilders yohoFilterFunctionBuilders = new YohoFilterFunctionBuilders();
         for (IScorer iScorer : scorers) {
-			if(iScorer!=null){
+            if (iScorer != null) {
                 iScorer.addScorer(yohoFilterFunctionBuilders);
             }
         }
@@ -148,7 +150,7 @@ public class FunctionScoreSearchHelper {
         scorers.add(searchScorerFactory.getOfflineSaleOnlyScorer());
         // 6、添加关键词完全匹配的打分器
         String query = MapUtils.getString(paramMap, SearchRequestParams.PARAM_SEARCH_QUERY);
-		if(StringUtils.isNotBlank(query)){
+        if (StringUtils.isNotBlank(query)) {
             scorers.add(searchScorerFactory.getCsBrandKeyWordScorer(query));
         }
         // 7、添加特殊店铺的打分器
@@ -217,13 +219,13 @@ public class FunctionScoreSearchHelper {
             return null;
         }
         // 2、获取PersonalizedSearch
-		PersonalizedSearch personalizedSearch = personalVectorFeatureSearch.getPersonalizedSearch(paramMap);
+        PersonalizedSearch personalizedSearch = personalVectorFeatureSearch.queryPersonalizedSearch(paramMap);
         if (personalizedSearch == null) {
             return null;
         }
         // 3、为个性化打分添加filter条件
         BoolQueryBuilder scoreFilter = productCountService.genScoreFilter(persionalFilter);
-		return searchScorerFactory.getFeatureFactorScorer(scoreFilter, personalizedSearch.getUserVectorFeature(), personalizedSearch.getVectorFeatureVersion());
+        return searchScorerFactory.getFeatureFactorScorer(scoreFilter, personalizedSearch.getVectorVersion(), personalizedSearch.getVectorFeatures());
     }
 
     /**
@@ -243,12 +245,17 @@ public class FunctionScoreSearchHelper {
         if (productVectorFeatures.length != 2) {
             return queryBuilder;
         }
-		IScorer scorer = searchScorerFactory.getFeatureFactorScorer(queryBuilder, productVectorFeatures[1], productVectorFeatures[0]);
+        // 3、参数检测
+        String featureVersion = productVectorFeatures[0];
+        List<Double> featureFactors = ConvertUtils.stringToDoubleList(productVectorFeatures[1], ",");
+        if (StringUtils.isBlank(featureVersion) || CollectionUtils.isEmpty(featureFactors)) {
+            return queryBuilder;
+        }
+        //4、构造scorer
+        IScorer scorer = searchScorerFactory.getFeatureFactorScorer(queryBuilder, featureVersion, featureFactors);
         YohoFilterFunctionBuilders yohoFilterFunctionBuilders = new YohoFilterFunctionBuilders();
-		if(scorer!=null){
         scorer.addScorer(yohoFilterFunctionBuilders);
-		}
-		FunctionScoreQueryBuilder functionScoreQueryBuilder = new FunctionScoreQueryBuilder(queryBuilder,yohoFilterFunctionBuilders.getFilterFunctionBuilders());
+        FunctionScoreQueryBuilder functionScoreQueryBuilder = new FunctionScoreQueryBuilder(queryBuilder, yohoFilterFunctionBuilders.getFilterFunctionBuilders());
         return functionScoreQueryBuilder;
     }
 

@@ -182,7 +182,7 @@ public class UserRecallResponseBuilder {
         //1、获取用户向量
         Map<String, String> paramMap = new HashMap<>();
         paramMap.put("uid", "" + uid);
-        PersonalizedSearch personalizedSearch = personalVectorFeatureSearch.getPersonalizedSearch(paramMap);
+        PersonalizedSearch personalizedSearch = personalVectorFeatureSearch.queryPersonalizedSearch(paramMap);
         UserFeatureFactor userFeatureFactor = new UserFeatureFactor(personalizedSearch);
         //2、计算相关性
         int recommendSknIndex = 10000;

 package com.yoho.search.service.recall.beans.persional;
 
 import com.yoho.search.base.helper.Word2VectorCalculator;
+import com.yoho.search.base.utils.ConvertUtils;
 import com.yoho.search.core.personalized.models.PersonalizedSearch;
 import com.yoho.search.service.recall.models.personal.UserFeatureFactor;
+import org.apache.commons.collections.CollectionUtils;
 import org.apache.commons.lang.StringUtils;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import org.springframework.stereotype.Component;
 
-import java.util.ArrayList;
 import java.util.List;
 
 @Component
@@ -21,23 +22,28 @@ public class ProductFeatureFactorComponent {
 
     public double calProductFeatureFactor(UserFeatureFactor userFeatureFactor, String productFeatureFactor) {
         try {
+            //商品向量不存在，则返回
+            if (StringUtils.isBlank(productFeatureFactor)) {
+                return 0;
+            }
             //用户向量不存在，则返回0
-            if (userFeatureFactor == null || StringUtils.isBlank(userFeatureFactor.getVectorFeatureVersion())) {
+            if (userFeatureFactor == null || StringUtils.isBlank(userFeatureFactor.getVectorVersion())) {
                 return 0;
             }
-            if (StringUtils.isBlank(productFeatureFactor)) {
+            if (CollectionUtils.isEmpty(userFeatureFactor.getVectorFeatureList())) {
                 return 0;
             }
-            String versionPrefix = userFeatureFactor.getVectorFeatureVersion() + "|";
+            //版本不匹配，则返回0
+            String versionPrefix = userFeatureFactor.getVectorVersion() + "|";
             if (!productFeatureFactor.trim().startsWith(versionPrefix)) {
                 return 0;
             }
-            String[] productFeatureFactorArr = productFeatureFactor.trim().substring(versionPrefix.length()).split(",");
-            List<Double> productFeatureVectors = this.arrayToList(productFeatureFactorArr);
-            if (productFeatureVectors == null || productFeatureVectors.size() != userFeatureFactor.getUserFeatureVectorList().size()) {
+            //执行计算
+            List<Double> productFeatureVectors = ConvertUtils.stringToDoubleList(productFeatureFactor.trim().substring(versionPrefix.length()), ",");
+            if (productFeatureVectors == null || productFeatureVectors.size() != userFeatureFactor.getVectorFeatureList().size()) {
                 return 0;
             }
-            double score = Word2VectorCalculator.calScore(userFeatureFactor.getUserFeatureVectorList(), userFeatureFactor.getUserFeatureVectorNorm(), productFeatureVectors);
+            double score = Word2VectorCalculator.calScore(userFeatureFactor.getVectorFeatureList(), userFeatureFactor.getUserFeatureVectorNorm(), productFeatureVectors);
             double finalScore = baseConstant + factorConstant * score;
             return finalScore;
         } catch (Exception e) {
@@ -46,22 +52,12 @@ public class ProductFeatureFactorComponent {
         }
     }
 
-    private List<Double> arrayToList(String[] productFeatureFactorStrArr) {
-        List<Double> results = new ArrayList<>();
-        if (productFeatureFactorStrArr == null) {
-            return results;
-        }
-        for (String productFeatureFactor : productFeatureFactorStrArr) {
-            results.add(Double.parseDouble(productFeatureFactor));
-        }
-        return results;
-    }
-
     public static void main(String[] args) {
-        PersonalizedSearch personalizedSearch = new PersonalizedSearch("1", "20180408", "0.342045,-0.547933,0.291732,-0.056515,-0.182701,0.31113,0.151578,0.087678,-0.045536,-0.525699,-0.394715,-0.103153,-0.05575,-0.540641,0.028046,-0.193109,-0.003591,0.180923,0.290261,0.532309,-0.202463,-0.047271,-0.246197,0.324561,0.188814,0.36475,0.079007,0.455753,-0.11848,-0.135874,-0.187155,-0.055342,-0.12525,0.210669,-0.388331,-0.197123,0.132309,-0.4231,0.217752,-0.203266,0.190836,0.373428,-0.0102,-0.038654,0.2379,0.044424,0.071826,-0.201054,0.257434,0.141901,-0.390064,0.437099,0.559701,-0.040162,-0.193089,0.442338,-0.141678,-0.049696,0.315545,-0.028972,0.278694,-0.064345,-0.327943,0.103025,-0.40344,-0.34269,-0.237931,0.287046,0.139693,-0.38454,0.019959,-0.156907,0.374996,-0.074558,-0.019391,0.050522,0.315171,0.211605,-0.15418,0.502362,0.10184,0.153274,0.592659,-0.010284,0.28029,0.319741,-0.164559,0.286884,0.420483,-0.628866,-0.172259,0.027954,-0.411674,0.376585,0.322832,0.352039,0.078705,0.045152,0.139083,-0.164182");
+        List<Double> array = ConvertUtils.stringToDoubleList("0.342045,-0.547933,0.291732,-0.056515,-0.182701,0.31113,0.151578,0.087678,-0.045536,-0.525699,-0.394715,-0.103153,-0.05575,-0.540641,0.028046,-0.193109,-0.003591,0.180923,0.290261,0.532309,-0.202463,-0.047271,-0.246197,0.324561,0.188814,0.36475,0.079007,0.455753,-0.11848,-0.135874,-0.187155,-0.055342,-0.12525,0.210669,-0.388331,-0.197123,0.132309,-0.4231,0.217752,-0.203266,0.190836,0.373428,-0.0102,-0.038654,0.2379,0.044424,0.071826,-0.201054,0.257434,0.141901,-0.390064,0.437099,0.559701,-0.040162,-0.193089,0.442338,-0.141678,-0.049696,0.315545,-0.028972,0.278694,-0.064345,-0.327943,0.103025,-0.40344,-0.34269,-0.237931,0.287046,0.139693,-0.38454,0.019959,-0.156907,0.374996,-0.074558,-0.019391,0.050522,0.315171,0.211605,-0.15418,0.502362,0.10184,0.153274,0.592659,-0.010284,0.28029,0.319741,-0.164559,0.286884,0.420483,-0.628866,-0.172259,0.027954,-0.411674,0.376585,0.322832,0.352039,0.078705,0.045152,0.139083,-0.164182", ",");
+        PersonalizedSearch personalizedSearch = new PersonalizedSearch("1", "20180408", array);
         UserFeatureFactor userFeatureFactor = new UserFeatureFactor(personalizedSearch);
-        String productFeatureFactor = "20180408|0.342045,-0.547933,0.291732,-0.056515,-0.182701,0.31113,0.151578,0.087678,-0.045536,-0.525699,-0.394715,-0.103153,-0.05575,-0.540641,0.028046,-0.193109,-0.003591,0.180923,0.290261,0.532309,-0.202463,-0.047271,-0.246197,0.324561,0.188814,0.36475,0.079007,0.455753,-0.11848,-0.135874,-0.187155,-0.055342,-0.12525,0.210669,-0.388331,-0.197123,0.132309,-0.4231,0.217752,-0.203266,0.190836,0.373428,-0.0102,-0.038654,0.2379,0.044424,0.071826,-0.201054,0.257434,0.141901,-0.390064,0.437099,0.559701,-0.040162,-0.193089,0.442338,-0.141678,-0.049696,0.315545,-0.028972,0.278694,-0.064345,-0.327943,0.103025,-0.40344,-0.34269,-0.237931,0.287046,0.139693,-0.38454,0.019959,-0.156907,0.374996,-0.074558,-0.019391,0.050522,0.315171,0.211605,-0.15418,0.502362,0.10184,0.153274,0.592659,-0.010284,0.28029,0.319741,-0.164559,0.286884,0.420483,-0.628866,-0.172259,0.027954,-0.411674,0.376585,0.322832,0.352039,0.078705,0.045152,0.139083,-0.164182";
-        System.out.println(new ProductFeatureFactorComponent().calProductFeatureFactor(userFeatureFactor,productFeatureFactor));
+        String productFeatureFactor = "20180408|1.342045,-0.547933,0.291732,-0.056515,-0.182701,0.31113,0.151578,0.087678,-0.045536,-0.525699,-0.394715,-0.103153,-0.05575,-0.540641,0.028046,-0.193109,-0.003591,0.180923,0.290261,0.532309,-0.202463,-0.047271,-0.246197,0.324561,0.188814,0.36475,0.079007,0.455753,-0.11848,-0.135874,-0.187155,-0.055342,-0.12525,0.210669,-0.388331,-0.197123,0.132309,-0.4231,0.217752,-0.203266,0.190836,0.373428,-0.0102,-0.038654,0.2379,0.044424,0.071826,-0.201054,0.257434,0.141901,-0.390064,0.437099,0.559701,-0.040162,-0.193089,0.442338,-0.141678,-0.049696,0.315545,-0.028972,0.278694,-0.064345,-0.327943,0.103025,-0.40344,-0.34269,-0.237931,0.287046,0.139693,-0.38454,0.019959,-0.156907,0.374996,-0.074558,-0.019391,0.050522,0.315171,0.211605,-0.15418,0.502362,0.10184,0.153274,0.592659,-0.010284,0.28029,0.319741,-0.164559,0.286884,0.420483,-0.628866,-0.172259,0.027954,-0.411674,0.376585,0.322832,0.352039,0.078705,0.045152,0.139083,-0.164182";
+        System.out.println(new ProductFeatureFactorComponent().calProductFeatureFactor(userFeatureFactor, productFeatureFactor));
     }
 
 }

 package com.yoho.search.service.recall.models.personal;
 
 import com.yoho.search.core.personalized.models.PersonalizedSearch;
-import org.apache.commons.lang.StringUtils;
+import org.apache.commons.collections.CollectionUtils;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import java.util.ArrayList;
 import java.util.List;
 
 public class UserFeatureFactor {
 
     private static final Logger logger = LoggerFactory.getLogger(UserFeatureFactor.class);
 
-	private List<Double> userFeatureVectorList = null;
+    private String vectorVersion;
+    private List<Double> vectorFeatureList = null;
     private double userFeatureVectorNorm = 0.0D;
-	private String vectorFeatureVersion;
 
     public UserFeatureFactor(PersonalizedSearch personalizedSearch) {
         try {
             if (personalizedSearch == null) {
                 return;
             }
-			this.vectorFeatureVersion = personalizedSearch.getVectorFeatureVersion();
-			String userVectorFeature = personalizedSearch.getUserVectorFeature();
-			if(StringUtils.isBlank(userVectorFeature)){
+            this.vectorVersion = personalizedSearch.getVectorVersion();
+            this.vectorFeatureList = personalizedSearch.getVectorFeatures();
+            if (CollectionUtils.isEmpty(vectorFeatureList)) {
                 return;
             }
-			String[] userFeatureFactorStrArr = userVectorFeature.split(",");
-			if(userFeatureFactorStrArr.length==0){
-				return;
-			}
-			userFeatureVectorList = new ArrayList<>(userFeatureFactorStrArr.length);
-			for (String userFeatureFactorStr : userFeatureFactorStrArr) {
-				double userFeatureVector = Double.parseDouble(userFeatureFactorStr.trim());
-				userFeatureVectorList.add(userFeatureVector);
-				userFeatureVectorNorm += userFeatureVector * userFeatureVector;
+            for (Double vectorFeature : vectorFeatureList) {
+                userFeatureVectorNorm += vectorFeature * vectorFeature;
             }
-		}catch (Exception e){
+        } catch (Exception e) {
             logger.error(e.getMessage());
         }
     }
 
-	public List<Double> getUserFeatureVectorList() {
-		return userFeatureVectorList;
+    public String getVectorVersion() {
+        return vectorVersion;
+    }
+
+    public List<Double> getVectorFeatureList() {
+        return vectorFeatureList;
     }
 
     public double getUserFeatureVectorNorm() {
         return userFeatureVectorNorm;
     }
 
-	public String getVectorFeatureVersion() {
-		return vectorFeatureVersion;
-	}
 }

@@ -102,8 +102,8 @@ public class SearchScorerFactory {
 	}
 
 	// 获取【向量余弦夹角】的打分器
-	public IScorer getFeatureFactorScorer(QueryBuilder scoreFilter, String featureFactors, String featureVersion) {
-		return new FeatureFactorScorer(scoreFilter, featureFactors, featureVersion);
+	public IScorer getFeatureFactorScorer(QueryBuilder scoreFilter,String featureVersion, List<Double> featureFactors) {
+		return new FeatureFactorScorer(scoreFilter,featureVersion,featureFactors);
 	}
 
 	public IScorer getOfflineSaleOnlyScorer() {

 package com.yoho.search.service.scorer.impl;
 
 import java.util.HashMap;
+import java.util.List;
 import java.util.Map;
 
+import org.apache.commons.collections.CollectionUtils;
+import org.apache.commons.lang.StringUtils;
 import org.elasticsearch.index.query.QueryBuilder;
 import org.elasticsearch.index.query.functionscore.ScoreFunctionBuilders;
 import org.elasticsearch.script.Script;
@@ -21,22 +24,22 @@ public class FeatureFactorScorer implements IScorer {
 	private static final Double FACTOR_CONSTANT = 0.4D;
 
 	private QueryBuilder scoreFilter;
-	private String featureFactors;
 	private String featureVersion;
+	private String featureFactorString;
 
-	public FeatureFactorScorer(QueryBuilder scoreFilter, String featureFactors, String featureVersion) {
+	public FeatureFactorScorer(QueryBuilder scoreFilter, String featureVersion,List<Double> featureFactors) {
 		super();
 		this.scoreFilter = scoreFilter;
-		this.featureFactors = featureFactors;
 		this.featureVersion = featureVersion;
+		this.featureFactorString = StringUtils.join(featureFactors,",");
 	}
 
 	@Override
 	public void addScorer(YohoFilterFunctionBuilders yohoFilterFunctionBuilders) {
 		Map<String, Object> scriptParams = new HashMap<>();
 		scriptParams.put("field", "productFeatureFactor");
-		scriptParams.put("userFeatureFactors", featureFactors);
 		scriptParams.put("vectorFeatureVersion", featureVersion);
+		scriptParams.put("userFeatureFactors", featureFactorString);
 		scriptParams.put("baseConstant", BASE_CONSTANT);
 		scriptParams.put("factorConstant", FACTOR_CONSTANT);
 		Script script = new Script(ScriptType.INLINE, "native", "feature_factor_vector_score", scriptParams);

 package com.yoho.search.service.scorer.personal;
 
 import com.yoho.search.aop.cache.SearchCacheAble;
+import com.yoho.search.base.utils.ConvertUtils;
 import com.yoho.search.cache.CacheType;
 import com.yoho.search.core.personalized.models.PersonalizedSearch;
 import com.yoho.search.core.personalized.service.BidataServiceCaller;
 import com.yoho.search.core.personalized.service.PersonalVersionManager;
+import org.apache.commons.collections.CollectionUtils;
 import org.apache.commons.lang3.StringUtils;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.stereotype.Service;
 
+import java.util.List;
 import java.util.Map;
 
 @Service
@@ -23,8 +26,8 @@ public class PersonalVectorFeatureSearch {
 	@Autowired
 	private BidataServiceCaller bidataServiceCaller;
 
-	@SearchCacheAble(cacheInMinute = 30, cacheName = "PERSIONAL_VECTOR", returnClass = PersonalizedSearch.class, cacheType = CacheType.SEARCH_REDIS, includeParams = { "uid" })
-	public PersonalizedSearch getPersonalizedSearch(Map<String, String> paramMap) {
+	@SearchCacheAble(cacheInMinute = 30, cacheName = "USER_PERSIONAL_VECTOR", returnClass = PersonalizedSearch.class, cacheType = CacheType.SEARCH_REDIS, includeParams = { "uid" })
+	public PersonalizedSearch queryPersonalizedSearch(Map<String, String> paramMap) {
 		try {
 			// 1、参数校验
 			String uid = paramMap.get("uid");
@@ -41,7 +44,11 @@ public class PersonalVectorFeatureSearch {
 			if (StringUtils.isEmpty(userVectorFeature)) {
 				return null;
 			}
-			return new PersonalizedSearch(uid, vectorFeatureVersion, userVectorFeature);
+			List<Double> doubleList = ConvertUtils.stringToDoubleList(userVectorFeature,",");
+			if(CollectionUtils.isEmpty(doubleList)){
+				return null;
+			}
+			return new PersonalizedSearch(uid, vectorFeatureVersion, doubleList);
 		} catch (Exception e) {
 			logger.error(e.getMessage(),e);
 			return null;