找相似功能优化

 package com.yoho.search.service.personalized;
 
-import com.yoho.search.base.utils.HttpServletRequestUtils;
-import com.yoho.search.service.service.SearchDynamicConfigService;
+import java.util.HashMap;
+import java.util.Map;
 
 import org.apache.commons.lang3.StringUtils;
 import org.elasticsearch.index.query.functionscore.FunctionScoreQueryBuilder;
@@ -13,141 +13,163 @@ import org.slf4j.LoggerFactory;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.stereotype.Service;
 
-import java.util.HashMap;
-import java.util.Map;
-
+import com.yoho.search.base.utils.HttpServletRequestUtils;
+import com.yoho.search.service.service.SearchDynamicConfigService;
 
 @Service
 public class PersonalVectorFeatureSearch {
-    
-    private static final Logger PERSONALIZED = LoggerFactory.getLogger("PERSONALIZED");
-
-    private static final Double BASE_CONSTANT = 1.0D;
-
-    private static final Double FACTOR_CONSTANT = 1.0D;
-
-    @Autowired
-    private SearchDynamicConfigService searchDynamicConfigService;
-
-    @Autowired
-    private PersonalizedRedisService personalizedRedisService;
-
-    public void addPersonalizedScriptScore(FunctionScoreQueryBuilder functionScoreQueryBuilder, Map<String,String> paramMap) {
-        // 1. 获取特征向量版本（即生成时间，该时间需要与skn的生成时间一致才有意义）
-        String vectorFeatureVersion = searchDynamicConfigService.personalizedSearchVersion();
-        if (StringUtils.isEmpty(vectorFeatureVersion) || "-1".equals(vectorFeatureVersion)) {
-            return;
-        }
-        
-        String uid = paramMap.get("uid");
-        
-        // 2. 获取用户的特征向量
-        String userVectorFeature = personalizedRedisService.getUserVectorFeature(uid, vectorFeatureVersion);
-        if (StringUtils.isEmpty(userVectorFeature)) {
-            return;
-        }
-       
-        PERSONALIZED.info("do personal search , paramString is [{}]", HttpServletRequestUtils.genParamString(paramMap));
-        
-        // 3. 传入参数调用脚本
-        // field -> productindex索引种保存skn特征向量的字段名
-        // userFeatureFactors -> 用户特征向量值，多个值之间用逗号分隔
-        // vectorFeatureVersion -> 用户特征向量版本，当与skn的版本一致才计算相关性
-        // baseConstant,factorConstant -> 相关性常量和系数，计算规则为 baseConstant + factorConstant * cos(Vuser, Vskn)
-        Map<String, Object> scriptParams = new HashMap<>();
-        scriptParams.put("field", "productFeatureFactor");
-        scriptParams.put("userFeatureFactors", userVectorFeature);
-        scriptParams.put("vectorFeatureVersion", vectorFeatureVersion);
-        scriptParams.put("baseConstant", BASE_CONSTANT);
-        scriptParams.put("factorConstant", FACTOR_CONSTANT);
-        Script script = new Script("feature_factor_vector_score", ScriptService.ScriptType.INLINE, "native", scriptParams);
-        functionScoreQueryBuilder.add(ScoreFunctionBuilders.scriptFunction(script));
-    }
-
-    /**
-     * 根据UID和SKN列表获取特征相关性评分，该方法用于测试和问题定位
-     *
-     * @param uid                     用户标识
-     * @param productVectorFeatureMap 商品特征map
-     * @return 相关性计算结果
-     */
-    public Map<String, Object> calVectorFeature(String uid, String version, Map<String, String> productVectorFeatureMap) {
-        Map<String, Object> scoreMap = new HashMap<>();
-        if (productVectorFeatureMap == null || productVectorFeatureMap.isEmpty()) {
-            scoreMap.put("productVectorFeatureMap", "empty");
-            return scoreMap;
-        }
-
-
-        String vectorFeatureVersion = StringUtils.isNotEmpty(version) ? version : searchDynamicConfigService.personalizedSearchVersion();
-        scoreMap.put("vectorFeatureVersion", vectorFeatureVersion);
-        if (StringUtils.isEmpty(vectorFeatureVersion) || "-1".equals(vectorFeatureVersion)) {
-            return scoreMap;
-        }
-
-        // 2. 获取用户的特征向量
-        String userVectorFeature = personalizedRedisService.getUserVectorFeature(uid, vectorFeatureVersion);
-        scoreMap.put("userVectorFeature", userVectorFeature);
-        if (StringUtils.isEmpty(userVectorFeature)) {
-            return scoreMap;
-        }
-
-        String[] userFeatureFactorArr = userVectorFeature.split(",");
-        double tempUserFeatureVectorNorm = 0.0D;
-        int dimensionOfFactors = userFeatureFactorArr.length;
-        double[] userFeatureFactors = new double[dimensionOfFactors];
-        double temp;
-        for (int index = 0; index < dimensionOfFactors; index++) {
-            temp = Double.parseDouble(userFeatureFactorArr[index].trim());
-            userFeatureFactors[index] = temp;
-            tempUserFeatureVectorNorm += temp * temp;
-        }
-
-        final double userFeatureVectorNorm = tempUserFeatureVectorNorm;
-        scoreMap.put("userFeatureVectorNorm", userFeatureVectorNorm);
-        scoreMap.put("dimensionOfFactors", dimensionOfFactors);
-
-        // 3. 计算相关性得分
-        productVectorFeatureMap.forEach((skn, vector) -> {
-            Map<String, Object> content = new HashMap<String, Object>();
-            content.put("vector", vector);
-            content.put("score", calculateScore(vector, vectorFeatureVersion, userFeatureFactors, userFeatureVectorNorm));
-            scoreMap.put(skn, content);
-        });
-
-        return scoreMap;
-    }
-
-    public double calculateScore(String productFeatureFactor, String vectorFeatureVersion, double[] userFeatureFactors, double userFeatureVectorNorm) {
-        if (productFeatureFactor == null || productFeatureFactor.trim().isEmpty()) {
-            return BASE_CONSTANT;
-        }
-
-        String versionPrefix = vectorFeatureVersion + "|";
-        if (!productFeatureFactor.trim().startsWith(versionPrefix)) {
-            return BASE_CONSTANT;
-        }
-
-        String[] productFeatureFactorArr = productFeatureFactor.trim().substring(versionPrefix.length()).split(",");
-        if (productFeatureFactorArr == null || productFeatureFactorArr.length != userFeatureFactors.length) {
-            return BASE_CONSTANT;
-        }
-
-        double prodFeatureVectorNorm = 0.0D;
-        double productiveSum = 0.0D;
-        double tempProdFactor;
-        for (int i = 0; i < userFeatureFactors.length; i++) {
-            tempProdFactor = Double.parseDouble(productFeatureFactorArr[i].trim());
-            productiveSum += tempProdFactor * userFeatureFactors[i];
-            prodFeatureVectorNorm += tempProdFactor * tempProdFactor;
-        }
-
-        if (prodFeatureVectorNorm == 0) {
-            return BASE_CONSTANT;
-        }
-
-        double cosScore = productiveSum / (Math.sqrt(prodFeatureVectorNorm) * Math.sqrt(userFeatureVectorNorm));
-        double finalScore = BASE_CONSTANT + FACTOR_CONSTANT * cosScore;
-        return finalScore;
-    }
+
+	private static final Logger PERSONALIZED = LoggerFactory.getLogger("PERSONALIZED");
+
+	private static final Double BASE_CONSTANT = 1.0D;
+
+	private static final Double FACTOR_CONSTANT = 1.0D;
+
+	@Autowired
+	private SearchDynamicConfigService searchDynamicConfigService;
+
+	@Autowired
+	private PersonalizedRedisService personalizedRedisService;
+
+	public void addPersonalizedScriptScore(FunctionScoreQueryBuilder functionScoreQueryBuilder, Map<String, String> paramMap) {
+		// 1. 获取特征向量版本（即生成时间，该时间需要与skn的生成时间一致才有意义）
+		String vectorFeatureVersion = searchDynamicConfigService.personalizedSearchVersion();
+		if (StringUtils.isEmpty(vectorFeatureVersion) || "-1".equals(vectorFeatureVersion)) {
+			return;
+		}
+
+		String uid = paramMap.get("uid");
+
+		// 2. 获取用户的特征向量
+		String userVectorFeature = personalizedRedisService.getUserVectorFeature(uid, vectorFeatureVersion);
+		if (StringUtils.isEmpty(userVectorFeature)) {
+			return;
+		}
+
+		PERSONALIZED.info("do personal search , paramString is [{}]", HttpServletRequestUtils.genParamString(paramMap));
+
+		// 3. 传入参数调用脚本
+		// field -> productindex索引种保存skn特征向量的字段名
+		// userFeatureFactors -> 用户特征向量值，多个值之间用逗号分隔
+		// vectorFeatureVersion -> 用户特征向量版本，当与skn的版本一致才计算相关性
+		// baseConstant,factorConstant -> 相关性常量和系数，计算规则为 baseConstant +
+		// factorConstant * cos(Vuser, Vskn)
+		Map<String, Object> scriptParams = new HashMap<>();
+		scriptParams.put("field", "productFeatureFactor");
+		scriptParams.put("userFeatureFactors", userVectorFeature);
+		scriptParams.put("vectorFeatureVersion", vectorFeatureVersion);
+		scriptParams.put("baseConstant", BASE_CONSTANT);
+		scriptParams.put("factorConstant", FACTOR_CONSTANT);
+		Script script = new Script("feature_factor_vector_score", ScriptService.ScriptType.INLINE, "native", scriptParams);
+		functionScoreQueryBuilder.add(ScoreFunctionBuilders.scriptFunction(script));
+	}
+
+	public void addPersonalizedScriptScoreUserProductFeature(FunctionScoreQueryBuilder functionScoreQueryBuilder, String productVectorFeature) {
+		// 1. 获取特征向量版本（即生成时间，该时间需要与skn的生成时间一致才有意义）
+		String vectorFeatureVersion = searchDynamicConfigService.personalizedSearchVersion();
+		if (StringUtils.isEmpty(vectorFeatureVersion) || "-1".equals(vectorFeatureVersion)) {
+			return;
+		}
+		// 2. 获取商品特征向量
+		if(StringUtils.isBlank(productVectorFeature)){
+			return;
+		}
+		// 3. 传入参数调用脚本，以商品特征代替用户特征
+		Map<String, Object> scriptParams = new HashMap<>();
+		scriptParams.put("field", "productFeatureFactor");
+		scriptParams.put("userFeatureFactors", productVectorFeature);
+		scriptParams.put("vectorFeatureVersion", vectorFeatureVersion);
+		scriptParams.put("baseConstant", BASE_CONSTANT);
+		scriptParams.put("factorConstant", FACTOR_CONSTANT);
+		Script script = new Script("feature_factor_vector_score", ScriptService.ScriptType.INLINE, "native", scriptParams);
+		functionScoreQueryBuilder.add(ScoreFunctionBuilders.scriptFunction(script));
+	}
+
+	/**
+	 * 根据UID和SKN列表获取特征相关性评分，该方法用于测试和问题定位
+	 *
+	 * @param uid
+	 *            用户标识
+	 * @param productVectorFeatureMap
+	 *            商品特征map
+	 * @return 相关性计算结果
+	 */
+	public Map<String, Object> calVectorFeature(String uid, String version, Map<String, String> productVectorFeatureMap) {
+		Map<String, Object> scoreMap = new HashMap<>();
+		if (productVectorFeatureMap == null || productVectorFeatureMap.isEmpty()) {
+			scoreMap.put("productVectorFeatureMap", "empty");
+			return scoreMap;
+		}
+
+		String vectorFeatureVersion = StringUtils.isNotEmpty(version) ? version : searchDynamicConfigService.personalizedSearchVersion();
+		scoreMap.put("vectorFeatureVersion", vectorFeatureVersion);
+		if (StringUtils.isEmpty(vectorFeatureVersion) || "-1".equals(vectorFeatureVersion)) {
+			return scoreMap;
+		}
+
+		// 2. 获取用户的特征向量
+		String userVectorFeature = personalizedRedisService.getUserVectorFeature(uid, vectorFeatureVersion);
+		scoreMap.put("userVectorFeature", userVectorFeature);
+		if (StringUtils.isEmpty(userVectorFeature)) {
+			return scoreMap;
+		}
+
+		String[] userFeatureFactorArr = userVectorFeature.split(",");
+		double tempUserFeatureVectorNorm = 0.0D;
+		int dimensionOfFactors = userFeatureFactorArr.length;
+		double[] userFeatureFactors = new double[dimensionOfFactors];
+		double temp;
+		for (int index = 0; index < dimensionOfFactors; index++) {
+			temp = Double.parseDouble(userFeatureFactorArr[index].trim());
+			userFeatureFactors[index] = temp;
+			tempUserFeatureVectorNorm += temp * temp;
+		}
+
+		final double userFeatureVectorNorm = tempUserFeatureVectorNorm;
+		scoreMap.put("userFeatureVectorNorm", userFeatureVectorNorm);
+		scoreMap.put("dimensionOfFactors", dimensionOfFactors);
+
+		// 3. 计算相关性得分
+		productVectorFeatureMap.forEach((skn, vector) -> {
+			Map<String, Object> content = new HashMap<String, Object>();
+			content.put("vector", vector);
+			content.put("score", calculateScore(vector, vectorFeatureVersion, userFeatureFactors, userFeatureVectorNorm));
+			scoreMap.put(skn, content);
+		});
+
+		return scoreMap;
+	}
+
+	public double calculateScore(String productFeatureFactor, String vectorFeatureVersion, double[] userFeatureFactors, double userFeatureVectorNorm) {
+		if (productFeatureFactor == null || productFeatureFactor.trim().isEmpty()) {
+			return BASE_CONSTANT;
+		}
+
+		String versionPrefix = vectorFeatureVersion + "|";
+		if (!productFeatureFactor.trim().startsWith(versionPrefix)) {
+			return BASE_CONSTANT;
+		}
+
+		String[] productFeatureFactorArr = productFeatureFactor.trim().substring(versionPrefix.length()).split(",");
+		if (productFeatureFactorArr == null || productFeatureFactorArr.length != userFeatureFactors.length) {
+			return BASE_CONSTANT;
+		}
+
+		double prodFeatureVectorNorm = 0.0D;
+		double productiveSum = 0.0D;
+		double tempProdFactor;
+		for (int i = 0; i < userFeatureFactors.length; i++) {
+			tempProdFactor = Double.parseDouble(productFeatureFactorArr[i].trim());
+			productiveSum += tempProdFactor * userFeatureFactors[i];
+			prodFeatureVectorNorm += tempProdFactor * tempProdFactor;
+		}
+
+		if (prodFeatureVectorNorm == 0) {
+			return BASE_CONSTANT;
+		}
+
+		double cosScore = productiveSum / (Math.sqrt(prodFeatureVectorNorm) * Math.sqrt(userFeatureVectorNorm));
+		double finalScore = BASE_CONSTANT + FACTOR_CONSTANT * cosScore;
+		return finalScore;
+	}
 }

@@ -11,27 +11,39 @@ import org.springframework.web.bind.annotation.RequestMethod;
 import org.springframework.web.bind.annotation.ResponseBody;
 
 import com.yoho.search.service.downgrade.aop.DownGradeAble;
+import com.yoho.search.service.servicenew.ISearchLikeInShopService;
 import com.yoho.search.service.servicenew.ISearchLikeService;
 import com.yoho.search.service.utils.HttpServletRequestUtils;
 import com.yoho.search.service.vo.SearchApiResult;
 
 /**
  * 找相似功能
+ * 
  * @author hugufei
  */
 
 @Controller
 public class SearchLikeController {
-	
+
 	@Autowired
 	private ISearchLikeService searchLikeService;
-	
-	@DownGradeAble(key="searchLike")
+	@Autowired
+	private ISearchLikeInShopService searchLikeInShopService;
+
+	@DownGradeAble(key = "searchLike")
 	@RequestMapping(method = RequestMethod.GET, value = "/searchLike")
 	@ResponseBody
-	public SearchApiResult searchLike(HttpServletRequest request){
+	public SearchApiResult searchLike(HttpServletRequest request) {
 		Map<String, String> paramMap = HttpServletRequestUtils.transParamType(request);
 		return searchLikeService.searchLike(paramMap);
 	}
-	
+
+	@DownGradeAble(key = "searchLikeInShop")
+	@RequestMapping(method = RequestMethod.GET, value = "/searchLikeInShop")
+	@ResponseBody
+	public SearchApiResult searchLikeInShop(HttpServletRequest request) {
+		Map<String, String> paramMap = HttpServletRequestUtils.transParamType(request);
+		return searchLikeInShopService.searchLikeInShop(paramMap);
+	}
+
 }

@@ -29,7 +29,7 @@ public class FunctionScoreSearchHelper {
 	private SearchDynamicConfigService dynamicConfig;
 
 	static float globalWeight = 0.50f;
-	
+
 	private WeightBuilder genWeightFactorBuilder(float factor) {
 		return ScoreFunctionBuilders.weightFactorFunction(factor);
 	}
@@ -62,6 +62,19 @@ public class FunctionScoreSearchHelper {
 		return functionScoreQueryBuilder;
 	}
 
+	/**
+	 * 直接使用商品特征的向量用来做个性化打分
+	 * 
+	 * @param queryBuilder
+	 * @param productFeature
+	 * @return
+	 */
+	public QueryBuilder buildFunctionScoreQueryBuildWithProductFeature(QueryBuilder queryBuilder, String productFeature) {
+		FunctionScoreQueryBuilder functionScoreQueryBuilder = new FunctionScoreQueryBuilder(queryBuilder);
+		personalVectorFeatureSearch.addPersonalizedScriptScoreUserProductFeature(functionScoreQueryBuilder, productFeature);
+		return functionScoreQueryBuilder;
+	}
+
 	private QueryBuilder getPhysicalChannelQueryBuilder(Map<String, String> paramMap) {
 		String physicalChannel = paramMap.get(SearchRequestParams.PHYSICAL_CHANNEL);
 		BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();

+package com.yoho.search.service.servicenew;
+
+import java.util.Map;
+
+import com.yoho.search.service.vo.SearchApiResult;
+
+public interface ISearchLikeInShopService {
+	/**
+	 * 店铺内找相似功能
+	 * 
+	 * @param paramMap
+	 * @return
+	 */
+	public SearchApiResult searchLikeInShop(Map<String, String> paramMap);
+}

+package com.yoho.search.service.servicenew.impl;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+
+import javax.annotation.PostConstruct;
+
+import org.elasticsearch.index.query.BoolQueryBuilder;
+import org.elasticsearch.index.query.MultiMatchQueryBuilder;
+import org.elasticsearch.index.query.QueryBuilder;
+import org.elasticsearch.index.query.QueryBuilders;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.stereotype.Component;
+
+import com.alibaba.fastjson.JSONObject;
+import com.yoho.search.base.utils.ISearchConstants;
+import com.yoho.search.core.es.model.SearchParam;
+import com.yoho.search.core.es.model.SearchResult;
+import com.yoho.search.service.service.SearchCacheService;
+import com.yoho.search.service.service.SearchCommonService;
+import com.yoho.search.service.service.helper.AggProductListHelper;
+import com.yoho.search.service.service.helper.FunctionScoreSearchHelper;
+import com.yoho.search.service.service.helper.SearchCommonHelper;
+import com.yoho.search.service.service.helper.SearchServiceHelper;
+import com.yoho.search.service.service.helper.SearchSortHelper;
+import com.yoho.search.service.vo.SearchFieldBoost;
+
+@Component
+public class SearchLikeCommonService {
+
+	@Autowired
+	private SearchCommonHelper searchCommonHelper;
+	@Autowired
+	private SearchCommonService searchCommonService;
+	@Autowired
+	private SearchServiceHelper searchServiceHelper;
+	@Autowired
+	private SearchSortHelper searchSortHelper;
+	@Autowired
+	private SearchCacheService searchCacheService;
+	@Autowired
+	private FunctionScoreSearchHelper functionScoreSearchHelper;
+	@Autowired
+	private AggProductListHelper aggProductListHelper;
+
+	public List<SearchFieldBoost> searchFieldBoosts = new ArrayList<SearchFieldBoost>();
+
+	private int sortMaxBoost = 1000;
+	private int brandMaxBoost = 700;
+	private int productNameMaxBoost = 600;
+	private int standardOnlyNamesBoost = 500;
+	private int otherBoost = 150;
+
+	private void addSearchFieldBoost(List<String> searchFields, float maxBoost) {
+		float boost = maxBoost;
+		for (String searchField : searchFields) {
+			searchFieldBoosts.add(new SearchFieldBoost(searchField, boost));
+			boost = boost - 20;
+		}
+	}
+
+	@PostConstruct
+	void init() {
+		// 品类权重
+		List<String> searchFields = Arrays.asList("smallSort", "smallSort.smallSort_pinyin", "middleSort", "middleSort.middleSort_pinyin", "maxSort", "maxSort.maxSort_pinyin");
+		this.addSearchFieldBoost(searchFields, sortMaxBoost);
+
+		// 商品名称权重
+		searchFields = Arrays.asList("productName.productName_ansj");
+		this.addSearchFieldBoost(searchFields, productNameMaxBoost);
+
+		// 品牌权重
+		searchFields = Arrays.asList("brandName.brandName_lowercase", "brandName", "brandNameCn", "brandNameCn.brandNameCn_pinyin", "brandDomain", "brandNameEn");
+		this.addSearchFieldBoost(searchFields, brandMaxBoost);
+
+		// 规则权重
+		searchFields = Arrays.asList("standardOnlyNames.standardOnlyNames_ansj", "standardOnlyNames.standardOnlyNames_pinyin");
+		this.addSearchFieldBoost(searchFields, standardOnlyNamesBoost);
+
+		// 其他字段权重
+		searchFields = Arrays.asList("specialSearchField", "productKeyword", "brandKeyword", "genderS", "searchField_ansj", "searchField");
+		this.addSearchFieldBoost(searchFields, otherBoost);
+	}
+
+	/**
+	 * 获取SKN在ES中的原生信息
+	 * 
+	 * @param productSkn
+	 * @return
+	 */
+	public JSONObject getProductInfoInEs(String productSkn) {
+		SearchParam searchParam = new SearchParam();
+		searchParam.setQuery(QueryBuilders.matchAllQuery());
+		BoolQueryBuilder boolFilter = QueryBuilders.boolQuery();
+		boolFilter.must(QueryBuilders.termsQuery("productSkn", productSkn));
+		boolFilter.mustNot(QueryBuilders.termsQuery("isGlobal", "Y"));
+		searchParam.setFiter(boolFilter);
+		searchParam.setAggregationBuilders(null);
+		searchParam.setPage(0);
+		searchParam.setOffset(0);
+		searchParam.setSize(1);
+
+		String productIndexName = ISearchConstants.INDEX_NAME_PRODUCT_INDEX;
+		JSONObject jsonObject = searchCacheService.getJSONObjectFromCache(productIndexName, searchParam);
+		if (jsonObject != null) {
+			return jsonObject;
+		}
+		SearchResult searchResult = searchCommonService.doSearch(productIndexName, searchParam);
+		if (searchResult == null) {
+			return null;
+		}
+		List<Map<String, Object>> productLists = searchResult.getResultList();
+		if (productLists == null || productLists.isEmpty()) {
+			return null;
+		}
+		jsonObject = new JSONObject();
+		jsonObject.putAll(productLists.get(0));
+		searchCacheService.addJSONObjectToCache(productIndexName, searchParam, jsonObject);
+		return jsonObject;
+	}
+
+	/**
+	 * 根据ES的原生信息生成返回的结果
+	 * 
+	 * @param productInfoInEs
+	 * @return
+	 */
+	public Map<String, Object> genProductInfoResult(JSONObject productInfoInEs) {
+		List<Map<String, Object>> resultList = new ArrayList<Map<String, Object>>();
+		resultList.add(productInfoInEs);
+		List<Map<String, Object>> results = searchServiceHelper.getProductMapList(resultList);
+		if (results == null || results.isEmpty()) {
+			return null;
+		}
+		return results.get(0);
+	}
+
+	/**
+	 * 找相似相关的匹配打分策略
+	 * 
+	 * @param query
+	 * @param minimumShouldMatch
+	 * @param productFeatureFactor
+	 * @return
+	 */
+	public QueryBuilder genSearchLikeQueryBuilder(String query, String minimumShouldMatch, String productFeatureFactor) {
+		// 1、设置字段权重
+		MultiMatchQueryBuilder multiMatchQueryBuilder = QueryBuilders.multiMatchQuery(query);
+		multiMatchQueryBuilder.type(MultiMatchQueryBuilder.Type.CROSS_FIELDS);
+		for (SearchFieldBoost searchFieldBoost : searchFieldBoosts) {
+			multiMatchQueryBuilder.field(searchFieldBoost.getFieldName(), searchFieldBoost.getBoost());
+		}
+		// 2、设置匹配度
+		multiMatchQueryBuilder.minimumShouldMatch(minimumShouldMatch);
+		// 3、添加基于商品特征的个性化打分
+		QueryBuilder queryBuilder = functionScoreSearchHelper.buildFunctionScoreQueryBuildWithProductFeature(multiMatchQueryBuilder, productFeatureFactor);
+		return queryBuilder;
+	}
+
+	/**
+	 * 找相似默认的条件
+	 * 
+	 * @param productSkn
+	 * @return
+	 */
+	public BoolQueryBuilder genDefaultQueryBuilder(List<String> notProductSkns) {
+		BoolQueryBuilder boolFilter = QueryBuilders.boolQuery();
+		boolFilter.mustNot(QueryBuilders.termsQuery("isSeckill", "Y"));
+		boolFilter.mustNot(QueryBuilders.termsQuery("isGlobal", "Y"));
+		boolFilter.must(QueryBuilders.termQuery("status", 1));
+		boolFilter.must(QueryBuilders.termQuery("isOutlets", 2));
+		boolFilter.must(QueryBuilders.termQuery("attribute", 1));
+		boolFilter.must(QueryBuilders.rangeQuery("storageNum").gte(1));
+		if (notProductSkns != null && !notProductSkns.isEmpty()) {
+			boolFilter.mustNot(QueryBuilders.termsQuery("productSkn", notProductSkns));
+		}
+		return boolFilter;
+	}
+
+}

+package com.yoho.search.service.servicenew.impl;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+
+import org.apache.commons.lang.StringUtils;
+import org.elasticsearch.index.query.BoolQueryBuilder;
+import org.elasticsearch.index.query.QueryBuilder;
+import org.elasticsearch.index.query.QueryBuilders;
+import org.elasticsearch.search.sort.SortBuilder;
+import org.elasticsearch.search.sort.SortBuilders;
+import org.elasticsearch.search.sort.SortOrder;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.stereotype.Service;
+
+import com.alibaba.fastjson.JSONObject;
+import com.yoho.search.base.utils.ISearchConstants;
+import com.yoho.search.core.es.model.SearchParam;
+import com.yoho.search.core.es.model.SearchResult;
+import com.yoho.search.service.cache.CacheEnum;
+import com.yoho.search.service.service.SearchCacheService;
+import com.yoho.search.service.service.SearchCommonService;
+import com.yoho.search.service.service.helper.AggProductListHelper;
+import com.yoho.search.service.service.helper.FunctionScoreSearchHelper;
+import com.yoho.search.service.service.helper.SearchCommonHelper;
+import com.yoho.search.service.service.helper.SearchServiceHelper;
+import com.yoho.search.service.service.helper.SearchSortHelper;
+import com.yoho.search.service.servicenew.ISearchLikeInShopService;
+import com.yoho.search.service.utils.SearchRequestParams;
+import com.yoho.search.service.vo.SearchApiResult;
+
+@Service
+public class SearchLikeInShopServiceImpl implements ISearchLikeInShopService {
+
+	@Autowired
+	private SearchCommonHelper searchCommonHelper;
+	@Autowired
+	private SearchCommonService searchCommonService;
+	@Autowired
+	private SearchServiceHelper searchServiceHelper;
+	@Autowired
+	private SearchSortHelper searchSortHelper;
+	@Autowired
+	private SearchCacheService searchCacheService;
+	@Autowired
+	private FunctionScoreSearchHelper functionScoreSearchHelper;
+	@Autowired
+	private AggProductListHelper aggProductListHelper;
+	@Autowired
+	private SearchLikeCommonService searchLikeCommonService;
+
+	public boolean isLegalInteger(Integer id) {
+		if (id != null && id > 0) {
+			return true;
+		}
+		return false;
+	}
+
+	@Override
+	public SearchApiResult searchLikeInShop(Map<String, String> paramMap) {
+		// 1、获取参数
+		String productSkn = paramMap.get(SearchRequestParams.PARAM_SYNC_SKN);
+		if (StringUtils.isBlank(productSkn)) {
+			return new SearchApiResult().setCode(400).setMessage("请输入SKN");
+		}
+		// 2、获取当前查询的SKN的基本信息
+		JSONObject productInfoInEs = searchLikeCommonService.getProductInfoInEs(productSkn);
+		if (productInfoInEs == null) {
+			return new SearchApiResult().setCode(400).setMessage("SKN不存在");
+		}
+		// 3、检测分页参数
+		int pageSize = StringUtils.isBlank(paramMap.get("viewNum")) ? 60 : Integer.parseInt(paramMap.get("viewNum"));
+		int page = StringUtils.isBlank(paramMap.get("page")) ? 1 : Integer.parseInt(paramMap.get("page"));
+		if (page < 1 || pageSize < 0) {
+			return new SearchApiResult().setCode(400).setMessage("分页参数不合法");
+		}
+		if (pageSize > 60 || pageSize < 10) {
+			pageSize = 60;
+		}
+
+		// 4、设置SearchParam
+		SearchParam searchParam = new SearchParam();
+
+		// 4.1)设置query
+		String productName = productInfoInEs.getString("productName");
+		String standardOnlyNames = productInfoInEs.getString("standardOnlyNames");
+		String style = productInfoInEs.getString("style");
+		String brandName = productInfoInEs.getString("brandName");
+		String productFeatureFactor = productInfoInEs.getString("productFeatureFactor");
+		String query = brandName + "," + productName + "," + standardOnlyNames + "," + style;
+		QueryBuilder queryBuilder = searchLikeCommonService.genSearchLikeQueryBuilder(query, "40%", productFeatureFactor);
+		searchParam.setQuery(queryBuilder);
+		
+		// 5、设置filter
+		BoolQueryBuilder boolFilter = searchLikeCommonService.genDefaultQueryBuilder(Arrays.asList(productSkn));
+		Integer brandId = productInfoInEs.getInteger("brandId");
+		Integer shopId = productInfoInEs.getInteger("shopId");
+		if (isLegalInteger(shopId)) {
+			boolFilter.must(QueryBuilders.termQuery("shopId", shopId));
+		} else if (isLegalInteger(brandId)) {
+			boolFilter.must(QueryBuilders.termQuery("brandId", brandId));
+		}
+
+		// 6、设置排序规则[按打分排序]
+		List<SortBuilder> sortBuilders = new ArrayList<SortBuilder>();
+		sortBuilders.add(SortBuilders.fieldSort("_score").order(SortOrder.DESC));
+		searchParam.setSortBuilders(sortBuilders);
+		// 7、设置分页参数
+		searchParam.setPage(1);
+		searchParam.setOffset(0);
+		searchParam.setSize(pageSize);
+
+		// 8、从缓存中获取数据，有则直接返回
+		String productIndexName = ISearchConstants.INDEX_NAME_PRODUCT_INDEX;
+		CacheEnum cacheEnum = CacheEnum.EHCACHE;
+		JSONObject jsonObject = searchCacheService.getJSONObjectFromCache(cacheEnum, productIndexName, searchParam);
+		if (jsonObject != null) {
+			return new SearchApiResult().setData(jsonObject);
+		}
+		// 9、执行搜索
+		SearchResult searchResult = searchCommonService.doSearch(ISearchConstants.INDEX_NAME_PRODUCT_INDEX, searchParam);
+		if (searchResult == null) {
+			return new SearchApiResult().setData(null);
+		}
+		// 10、构造返回结果并加入缓存
+		List<Map<String, Object>> productList = searchServiceHelper.getProductMapList(searchResult.getResultList());
+		jsonObject = new JSONObject();
+		jsonObject.put("page", 1);
+		jsonObject.put("page_total", 1);
+		jsonObject.put("page_size", pageSize);
+		jsonObject.put("total", productList.size());
+		jsonObject.put("product_info", searchLikeCommonService.genProductInfoResult(productInfoInEs));
+		jsonObject.put("product_list", productList);
+		searchCacheService.addJSONObjectToCache(cacheEnum, productIndexName, searchParam, jsonObject);
+		return new SearchApiResult().setData(jsonObject);
+	}
+
+}

@@ -6,11 +6,8 @@ import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 
-import javax.annotation.PostConstruct;
-
 import org.apache.commons.lang.StringUtils;
 import org.elasticsearch.index.query.BoolQueryBuilder;
-import org.elasticsearch.index.query.MultiMatchQueryBuilder;
 import org.elasticsearch.index.query.QueryBuilder;
 import org.elasticsearch.index.query.QueryBuilders;
 import org.elasticsearch.search.aggregations.AbstractAggregationBuilder;
@@ -27,7 +24,6 @@ import org.springframework.stereotype.Service;
 
 import com.alibaba.fastjson.JSONArray;
 import com.alibaba.fastjson.JSONObject;
-import com.yoho.search.base.utils.ConvertUtils;
 import com.yoho.search.base.utils.ISearchConstants;
 import com.yoho.search.core.es.model.SearchParam;
 import com.yoho.search.core.es.model.SearchResult;
@@ -42,7 +38,6 @@ import com.yoho.search.service.service.helper.SearchSortHelper;
 import com.yoho.search.service.servicenew.ISearchLikeService;
 import com.yoho.search.service.utils.SearchRequestParams;
 import com.yoho.search.service.vo.SearchApiResult;
-import com.yoho.search.service.vo.SearchFieldBoost;
 
 @Service
 public class SearchLikeServiceImpl implements ISearchLikeService {
@@ -61,45 +56,8 @@ public class SearchLikeServiceImpl implements ISearchLikeService {
 	private FunctionScoreSearchHelper functionScoreSearchHelper;
 	@Autowired
 	private AggProductListHelper aggProductListHelper;
-
-	private List<SearchFieldBoost> searchFieldBoosts = new ArrayList<SearchFieldBoost>();
-
-	private int sortMaxBoost = 1000;
-	private int brandMaxBoost = 700;
-	private int productNameMaxBoost = 600;
-	private int standardOnlyNamesBoost = 500;
-	private int otherBoost = 150;
-
-	private void addSearchFieldBoost(List<String> searchFields, float maxBoost) {
-		float boost = maxBoost;
-		for (String searchField : searchFields) {
-			searchFieldBoosts.add(new SearchFieldBoost(searchField, boost));
-			boost = boost - 20;
-		}
-	}
-
-	@PostConstruct
-	void init() {
-		// 品类权重
-		List<String> searchFields = Arrays.asList("smallSort", "smallSort.smallSort_pinyin", "middleSort", "middleSort.middleSort_pinyin", "maxSort", "maxSort.maxSort_pinyin");
-		this.addSearchFieldBoost(searchFields, sortMaxBoost);
-
-		// 商品名称权重
-		searchFields = Arrays.asList("productName.productName_ansj");
-		this.addSearchFieldBoost(searchFields, productNameMaxBoost);
-
-		// 品牌权重
-		searchFields = Arrays.asList("brandName.brandName_lowercase", "brandName", "brandNameCn", "brandNameCn.brandNameCn_pinyin", "brandDomain", "brandNameEn");
-		this.addSearchFieldBoost(searchFields, brandMaxBoost);
-
-		// 规则权重
-		searchFields = Arrays.asList("standardOnlyNames.standardOnlyNames_ansj", "standardOnlyNames.standardOnlyNames_pinyin");
-		this.addSearchFieldBoost(searchFields, standardOnlyNamesBoost);
-
-		// 其他字段权重
-		searchFields = Arrays.asList("specialSearchField", "productKeyword", "brandKeyword", "genderS", "searchField_ansj", "searchField");
-		this.addSearchFieldBoost(searchFields, otherBoost);
-	}
+	@Autowired
+	private SearchLikeCommonService searchLikeCommonService;
 
 	@Override
 	public SearchApiResult searchLike(Map<String, String> paramMap) {
@@ -109,7 +67,7 @@ public class SearchLikeServiceImpl implements ISearchLikeService {
 			return new SearchApiResult().setCode(400).setMessage("请输入SKN");
 		}
 		// 2、获取当前查询的SKN的基本信息
-		JSONObject productInfoInEs = this.getProductInfoInEs(productSkn);
+		JSONObject productInfoInEs = searchLikeCommonService.getProductInfoInEs(productSkn);
 		if (productInfoInEs == null) {
 			return new SearchApiResult().setCode(400).setMessage("SKN不存在");
 		}
@@ -123,8 +81,8 @@ public class SearchLikeServiceImpl implements ISearchLikeService {
 			pageSize = 60;
 		}
 
-		// 4、获取同一个品牌下一半商品
-		JSONArray inBrandProductList = this.getProductListInBrand(productInfoInEs, paramMap, pageSize / 2);
+		// 4、获取同一个品牌下最多5个商品
+		JSONArray inBrandProductList = this.getProductListInBrand(productInfoInEs, paramMap, 5);
 
 		// 5、获取不同品牌下的余下数量的商品[通过聚合的方式来查找]
 		List<String> notInProductSkns = new ArrayList<String>();
@@ -145,58 +103,11 @@ public class SearchLikeServiceImpl implements ISearchLikeService {
 		dataMap.put("page_total", 1);
 		dataMap.put("page_size", pageSize);
 		dataMap.put("total", productList.size());
-		dataMap.put("product_info", this.genProductInfoResult(productInfoInEs));
+		dataMap.put("product_info", searchLikeCommonService.genProductInfoResult(productInfoInEs));
 		dataMap.put("product_list", productList);
 		return new SearchApiResult().setData(dataMap);
 	}
 
-	private Map<String, Object> genProductInfoResult(JSONObject productInfoInEs) {
-		List<Map<String, Object>> resultList = new ArrayList<Map<String, Object>>();
-		resultList.add(productInfoInEs);
-		List<Map<String, Object>> results = searchServiceHelper.getProductMapList(resultList);
-		if (results == null || results.isEmpty()) {
-			return null;
-		}
-		return results.get(0);
-	}
-
-	/**
-	 * 获取SKN在ES中的原生信息
-	 * 
-	 * @param productSkn
-	 * @return
-	 */
-	private JSONObject getProductInfoInEs(String productSkn) {
-		SearchParam searchParam = new SearchParam();
-		searchParam.setQuery(QueryBuilders.matchAllQuery());
-		BoolQueryBuilder boolFilter = QueryBuilders.boolQuery();
-		boolFilter.must(QueryBuilders.termsQuery("productSkn", productSkn));
-		boolFilter.mustNot(QueryBuilders.termsQuery("isGlobal", "Y"));
-		searchParam.setFiter(boolFilter);
-		searchParam.setAggregationBuilders(null);
-		searchParam.setPage(0);
-		searchParam.setOffset(0);
-		searchParam.setSize(1);
-
-		String productIndexName = ISearchConstants.INDEX_NAME_PRODUCT_INDEX;
-		JSONObject jsonObject = searchCacheService.getJSONObjectFromCache(productIndexName, searchParam);
-		if (jsonObject != null) {
-			return jsonObject;
-		}
-		SearchResult searchResult = searchCommonService.doSearch(productIndexName, searchParam);
-		if (searchResult == null) {
-			return null;
-		}
-		List<Map<String, Object>> productLists = searchResult.getResultList();
-		if (productLists == null || productLists.isEmpty()) {
-			return null;
-		}
-		jsonObject = new JSONObject();
-		jsonObject.putAll(productLists.get(0));
-		searchCacheService.addJSONObjectToCache(productIndexName, searchParam, jsonObject);
-		return jsonObject;
-	}
-
 	/**
 	 * 找出该品牌下的最多10个相似商品
 	 * 
@@ -310,27 +221,19 @@ public class SearchLikeServiceImpl implements ISearchLikeService {
 		String standardOnlyNames = productInfoInEs.getString("standardOnlyNames");
 		String style = productInfoInEs.getString("style");
 		String brandName = productInfoInEs.getString("brandName");
+		String productFeatureFactor = productInfoInEs.getString("productFeatureFactor");
 		String query = standardOnlyNames + "," + style;
 		if (isInBrand) {
 			query = brandName + "," + productName + "," + query;
 		} else {
 			query = productName.replaceAll(brandName, "") + "," + query;
 		}
-		// 2、设置字段权重
-		MultiMatchQueryBuilder multiMatchQueryBuilder = QueryBuilders.multiMatchQuery(query);
-		multiMatchQueryBuilder.type(MultiMatchQueryBuilder.Type.CROSS_FIELDS);
-		for (SearchFieldBoost searchFieldBoost : searchFieldBoosts) {
-			multiMatchQueryBuilder.field(searchFieldBoost.getFieldName(), searchFieldBoost.getBoost());
-		}
-		// 3、设置匹配度
-		if (isInBrand) {
-			multiMatchQueryBuilder.minimumShouldMatch("30%");
-		} else {
-			multiMatchQueryBuilder.minimumShouldMatch("30%");
+		// 2、设置匹配度
+		String minimumShouldMatch = "30%";
+		if (!isInBrand) {
+			minimumShouldMatch = "40%";
 		}
-		// 4、添加个性化打分
-		QueryBuilder queryBuilder = functionScoreSearchHelper.buildFunctionScoreQueryBuild(multiMatchQueryBuilder, paramMap);
-		return queryBuilder;
+		return searchLikeCommonService.genSearchLikeQueryBuilder(query, minimumShouldMatch, productFeatureFactor);
 	}
 
 	private List<String> getGenderInfo(String gender) {
@@ -344,7 +247,7 @@ public class SearchLikeServiceImpl implements ISearchLikeService {
 	}
 
 	private BoolQueryBuilder genBoolQueryBuilder(JSONObject productInfoInEs, Map<String, String> paramMap, List<String> notProductSkns, boolean isInBrand) {
-		BoolQueryBuilder boolFilter = QueryBuilders.boolQuery();
+		BoolQueryBuilder boolFilter = searchLikeCommonService.genDefaultQueryBuilder(notProductSkns);
 		// 1)设置此SKN相关的过滤条件
 		String gender = productInfoInEs.getString("gender");
 		List<String> genderList = this.getGenderInfo(gender);
@@ -358,29 +261,13 @@ public class SearchLikeServiceImpl implements ISearchLikeService {
 		sortFilter.should(QueryBuilders.termQuery("middleSortId", middle_sort_id));
 		sortFilter.should(QueryBuilders.termQuery("smallSortId", small_sort_id));
 		boolFilter.must(sortFilter);
-
-		// 3)设置默认的过滤条件
-		boolFilter.mustNot(QueryBuilders.termsQuery("isSeckill", "Y"));
-		boolFilter.mustNot(QueryBuilders.termsQuery("isGlobal", "Y"));
-		boolFilter.must(QueryBuilders.termQuery("status", 1));
-		boolFilter.must(QueryBuilders.termQuery("isOutlets", 2));
-		boolFilter.must(QueryBuilders.termQuery("attribute", 1));
-		boolFilter.must(QueryBuilders.rangeQuery("storageNum").gte(1));
-		if (notProductSkns != null && notProductSkns.size() > 0) {
-			boolFilter.mustNot(QueryBuilders.termsQuery("productSkn", notProductSkns.toArray(new String[notProductSkns.size()])));
-		}
-		// 4)设置品牌
+		// 3)设置品牌
 		Integer brandId = productInfoInEs.getInteger("brandId");
 		if (isInBrand) {
 			boolFilter.must(QueryBuilders.termQuery("brandId", brandId));
 		} else {
 			boolFilter.mustNot(QueryBuilders.termQuery("brandId", brandId));
 		}
-		// 5)设置店铺
-		if (!isInBrand && paramMap.containsKey(SearchRequestParams.PARAM_SEARCH_SHOP) && StringUtils.isNotBlank(paramMap.get(SearchRequestParams.PARAM_SEARCH_SHOP))) {
-			int[] shopids = ConvertUtils.stringToIntArray(paramMap.get(SearchRequestParams.PARAM_SEARCH_SHOP), ",");
-			boolFilter.must(QueryBuilders.termsQuery("shopId", shopids));
-		}
 		return boolFilter;
 	}
 
@@ -391,5 +278,4 @@ public class SearchLikeServiceImpl implements ISearchLikeService {
 		}
 		return jsonArray;
 	}
-
 }