Merge branch 'master' into zf_smart_search3

@@ -20,69 +20,92 @@ import com.yoho.search.service.service.SearchDynamicConfigService;
 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 = 0.8D;
 
 	@Autowired
 	private SearchDynamicConfigService searchDynamicConfigService;
-
 	@Autowired
 	private PersonalizedRedisService personalizedRedisService;
 
-	public void addPersonalizedScriptScore(FunctionScoreQueryBuilder functionScoreQueryBuilder, Map<String, String> paramMap) {
+	private void doAddPersionalFunctionScoreQueryBuilder(FunctionScoreQueryBuilder functionScoreQueryBuilder, String userVectorFeature, String vectorFeatureVersion) {
+		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));
+	}
+
+	/**
+	 * 通用的个性化打分逻辑
+	 * 
+	 * @param functionScoreQueryBuilder
+	 * @param paramMap
+	 */
+	public void addCommonPersonalizedScriptScore(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 common personal search , paramString is [{}]", HttpServletRequestUtils.genParamString(paramMap));
+		// 3、添加个性化打分脚本
+		this.doAddPersionalFunctionScoreQueryBuilder(functionScoreQueryBuilder, userVectorFeature, vectorFeatureVersion);
+	}
 
-		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));
+	/**
+	 * 活动个性化打分逻辑【使用用户购物车、收藏夹的维度】
+	 * 
+	 * @param functionScoreQueryBuilder
+	 * @param paramMap
+	 */
+	public void addActivityPersonalizedScriptScore(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.getUserActivityVectorFeature(uid, vectorFeatureVersion);
+		if (StringUtils.isEmpty(userVectorFeature)) {
+			userVectorFeature = personalizedRedisService.getUserVectorFeature(uid, vectorFeatureVersion);
+		}
+		if (StringUtils.isEmpty(userVectorFeature)) {
+			return;
+		}
+		PERSONALIZED.info("do activity personal search , paramString is [{}]", HttpServletRequestUtils.genParamString(paramMap));
+		// 3、添加个性化打分脚本
+		this.doAddPersionalFunctionScoreQueryBuilder(functionScoreQueryBuilder, userVectorFeature, vectorFeatureVersion);
 	}
 
-	public void addPersonalizedScriptScoreUserProductFeature(FunctionScoreQueryBuilder functionScoreQueryBuilder, String productVectorFeature) {
+	/**
+	 * 直接使用商品特征的个性化打分逻辑
+	 * 
+	 * @param functionScoreQueryBuilder
+	 * @param paramMap
+	 */
+	public void addProductPersonalizedScriptScore(FunctionScoreQueryBuilder functionScoreQueryBuilder, String productVectorFeature) {
 		// 1. 获取商品特征向量
-		if(StringUtils.isBlank(productVectorFeature)){
+		if (StringUtils.isBlank(productVectorFeature)) {
 			return;
 		}
 		// 2. 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");
-		
-		String [] productVectorFeatures = productVectorFeature.split("\\|",2);
-		if(productVectorFeatures.length!=2){
+		String[] productVectorFeatures = productVectorFeature.split("\\|", 2);
+		if (productVectorFeatures.length != 2) {
 			return;
 		}
-		scriptParams.put("userFeatureFactors", productVectorFeatures[1]);
-		scriptParams.put("vectorFeatureVersion", productVectorFeatures[0]);
-		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));
+		// 3、添加个性化打分脚本
+		this.doAddPersionalFunctionScoreQueryBuilder(functionScoreQueryBuilder, productVectorFeatures[1], productVectorFeatures[0]);
 	}
 
 	/**

@@ -20,6 +20,8 @@ public class PersonalizedRedisService {
 	// 保存用户特征向量的key格式，比如 “1022102:w2v:20170103” 值是一个字符串，各个值之间用逗号分隔
 	private static final String USER_FEATURE_KEY_TEMPLATE = "%s:w2v:%s";
 	
+	private static final String USER_ACTIVITY_FEATURE_KEY_TEMPLATE = "%s:w2v_f:%s";//活动相关的向量空间【购物车、收藏夹】
+	
 	private static final String USER_GENDER_KEY= "%s:gender";
 
 	@Autowired
@@ -30,6 +32,11 @@ public class PersonalizedRedisService {
 		return bigDataRedisOper.getValue(key);
 	}
 	
+	public String getUserActivityVectorFeature(String uid, String generateDate) {
+		String key = String.format(USER_ACTIVITY_FEATURE_KEY_TEMPLATE, uid, generateDate);
+		return bigDataRedisOper.getValue(key);
+	}
+	
 	/**
 	 * 获取用户性别维度
 	 * @param uid

@@ -60,7 +60,7 @@ public class FunctionScoreSearchHelper {
 		}
 		// 个性化搜索相关
 		if (searchCommonHelper.isNeedPersonalSearch(paramMap)) {
-			this.addPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
+			this.addCommonPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
 		}
 		// 针对全球购降分
 		if (searchCommonHelper.containGlobal(paramMap)) {
@@ -87,9 +87,9 @@ public class FunctionScoreSearchHelper {
 	 * @param functionScoreQueryBuilder
 	 * @param paramMap
 	 */
-	public void addPersonalizedScriptScore(FunctionScoreQueryBuilder functionScoreQueryBuilder, Map<String, String> paramMap) {
+	public void addCommonPersonalizedScriptScore(FunctionScoreQueryBuilder functionScoreQueryBuilder, Map<String, String> paramMap) {
 		// 个性化搜索相关
-		personalVectorFeatureSearch.addPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
+		personalVectorFeatureSearch.addCommonPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
 		if (searchCommonHelper.isNewRecPageDefault(paramMap)) {
 			this.addFirstShelveTimeScore(functionScoreQueryBuilder, newRecShelveTimeScore);
 		} else {
@@ -97,6 +97,10 @@ public class FunctionScoreSearchHelper {
 		}
 	}
 	
+	public void addActivityPersonalizedScriptScore(FunctionScoreQueryBuilder functionScoreQueryBuilder, Map<String, String> paramMap) {
+		personalVectorFeatureSearch.addActivityPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
+	}
+
 	private void addFirstShelveTimeScore(FunctionScoreQueryBuilder functionScoreQueryBuilder, FirstShelveTimeScore firstShelveTimeScore) {
 		int todayLastSecond = DateUtil.getLastTimeSecond(new Date());
 		int limitSecondValue = todayLastSecond - firstShelveTimeScore.getLimitDayCount() * oneDaySecondCount;
@@ -116,7 +120,7 @@ public class FunctionScoreSearchHelper {
 	 */
 	public QueryBuilder buildFunctionScoreQueryBuildWithProductFeature(QueryBuilder queryBuilder, String productFeature) {
 		FunctionScoreQueryBuilder functionScoreQueryBuilder = new FunctionScoreQueryBuilder(queryBuilder);
-		personalVectorFeatureSearch.addPersonalizedScriptScoreUserProductFeature(functionScoreQueryBuilder, productFeature);
+		personalVectorFeatureSearch.addProductPersonalizedScriptScore(functionScoreQueryBuilder, productFeature);
 		return functionScoreQueryBuilder;
 	}
 

@@ -11,6 +11,9 @@ import java.util.Map;
 import javax.annotation.PostConstruct;
 
 import org.apache.commons.lang.StringUtils;
+import org.elasticsearch.index.query.QueryBuilder;
+import org.elasticsearch.index.query.QueryBuilders;
+import org.elasticsearch.index.query.functionscore.FunctionScoreQueryBuilder;
 import org.elasticsearch.search.SearchHit;
 import org.elasticsearch.search.SearchHits;
 import org.elasticsearch.search.aggregations.AbstractAggregationBuilder;
@@ -40,6 +43,7 @@ import com.yoho.search.core.es.utils.IgnoreSomeException;
 import com.yoho.search.service.service.AggregationService;
 import com.yoho.search.service.service.SearchCacheService;
 import com.yoho.search.service.service.SearchCommonService;
+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;
@@ -64,6 +68,8 @@ public class AggProductListServiceImpl implements IAggProductListService, Applic
 	private SearchCommonService searchCommonService;
 	@Autowired
 	private SearchCacheService searchCacheService;
+	@Autowired
+	private FunctionScoreSearchHelper functionScoreSearchHelper;
 
 	private ApplicationEventPublisher publisher;
 
@@ -82,6 +88,12 @@ public class AggProductListServiceImpl implements IAggProductListService, Applic
 		aggTypeToEsField.put("brand", "brandId");
 	}
 	
+	private QueryBuilder builderActivityQueryBuilder(Map<String, String> paramMap) {
+		FunctionScoreQueryBuilder functionScoreQueryBuilder = new FunctionScoreQueryBuilder(QueryBuilders.matchAllQuery());
+		functionScoreSearchHelper.addActivityPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
+		return functionScoreQueryBuilder;
+	}
+
 	@Override
 	public SearchApiResult aggProductList(Map<String, String> paramMap) {
 		try {
@@ -103,7 +115,7 @@ public class AggProductListServiceImpl implements IAggProductListService, Applic
 
 			// 3、构造查询条件
 			SearchParam searchParam = new SearchParam();
-			searchParam.setQuery(searchServiceHelper.constructQueryBuilderForProductList(paramMap));// 支持个性化
+			searchParam.setQuery(this.builderActivityQueryBuilder(paramMap));// 活动特定的个性化
 			searchParam.setFiter(searchServiceHelper.constructFilterBuilder(paramMap, null));
 			searchParam.setSize(0);
 
@@ -201,7 +213,7 @@ public class AggProductListServiceImpl implements IAggProductListService, Applic
 			}
 			// 3、构造查询条件
 			SearchParam searchParam = new SearchParam();
-			searchParam.setQuery(searchServiceHelper.constructQueryBuilderForProductList(paramMap));// 支持个性化
+			searchParam.setQuery(this.builderActivityQueryBuilder(paramMap));// 活动特定的个性化
 			searchParam.setFiter(searchServiceHelper.constructFilterBuilder(paramMap, null));
 			searchParam.setSize(0);
 
@@ -218,7 +230,7 @@ public class AggProductListServiceImpl implements IAggProductListService, Applic
 			List<AbstractAggregationBuilder> list = new ArrayList<AbstractAggregationBuilder>();
 			// 4.1）构造父聚合:品牌或品类聚合【同时按子聚合的sort字段排序】
 			TermsBuilder brandAggregationBuilder = AggregationBuilders.terms(firstAggName).field("brandId").order(Terms.Order.aggregation("sort", sortOrder.equals(SortOrder.ASC)))
-					.size(200+pageSize);
+					.size(200 + pageSize);
 			// 4.2）添加子聚合:取得分最大的值
 			brandAggregationBuilder.subAggregation(AggregationBuilders.max("sort").field(sortField));
 			// 4.3）添加孙聚合：取打分最高的一个product
@@ -244,7 +256,7 @@ public class AggProductListServiceImpl implements IAggProductListService, Applic
 			if (!aggMaps.containsKey(firstAggName)) {
 				return searchApiResult.setData("");
 			}
-			List<Map<String, Object>> productList = this.getProductListOrderByScore(((MultiBucketsAggregation) aggMaps.get(firstAggName)), pageSize,sortField,sortOrder);
+			List<Map<String, Object>> productList = this.getProductListOrderByScore(((MultiBucketsAggregation) aggMaps.get(firstAggName)), pageSize, sortField, sortOrder);
 			jsonObject = new JSONObject();
 			jsonObject.put("total", pageSize);
 			jsonObject.put("page", 1);
@@ -302,19 +314,19 @@ public class AggProductListServiceImpl implements IAggProductListService, Applic
 		if (value == null) {
 			return 0;
 		}
-		if (value instanceof Float){
+		if (value instanceof Float) {
 			return ((Float) value).floatValue();
 		}
-		if (value instanceof Integer){
+		if (value instanceof Integer) {
 			return ((Integer) value);
 		}
-		if (value instanceof Long){
+		if (value instanceof Long) {
 			return ((Long) value);
 		}
-		if (value instanceof String){
+		if (value instanceof String) {
 			return Double.valueOf(value.toString());
 		}
-		if (value instanceof Double){
+		if (value instanceof Double) {
 			return Double.valueOf(value.toString());
 		}
 		return 0;

@@ -142,7 +142,7 @@ public class GoodProductListService implements IGoodProductsService {
 		}
 		// 加上个性化打分
 		if (searchCommonHelper.isNeedPersonalSearch(paramMap)) {
-			functionScoreSearchHelper.addPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
+			functionScoreSearchHelper.addCommonPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
 		}
 		return functionScoreQueryBuilder;
 	}
@@ -244,7 +244,7 @@ public class GoodProductListService implements IGoodProductsService {
 			functionScoreQueryBuilder.add(QueryBuilders.termsQuery("productSkn", productSkns.split(",")), ScoreFunctionBuilders.weightFactorFunction(100));
 		}
 		// 强制加上个性化打分
-		functionScoreSearchHelper.addPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
+		functionScoreSearchHelper.addCommonPersonalizedScriptScore(functionScoreQueryBuilder, paramMap);
 		searchParam.setQuery(functionScoreQueryBuilder);
 
 		// 4、设置聚合条件