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package com.yoho.search.service.scorer.personal;
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import com.yoho.search.base.utils.DateUtil;
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import com.yoho.search.cache.CacheType;
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import com.yoho.search.aop.cache.SearchCacheAble;
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import com.yoho.search.core.personalized.BigDataRedisService;
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import com.yoho.search.core.personalized.PersonalizedSearch;
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import com.yoho.search.core.personalized.version.PersonalVersionManager;
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import com.yoho.search.cache.CacheType;
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import com.yoho.search.core.personalized.models.PersonalizedSearch;
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import com.yoho.search.core.personalized.service.BidataServiceCaller;
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import com.yoho.search.core.personalized.service.PersonalVersionManager;
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import org.apache.commons.lang3.StringUtils;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.stereotype.Service;
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import java.util.Calendar;
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import java.util.HashMap;
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import java.util.Map;
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@Service
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@@ -21,13 +18,10 @@ public class PersonalVectorFeatureSearch { |
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private static final Logger logger = LoggerFactory.getLogger(PersonalVectorFeatureSearch.class);
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private static final Double BASE_CONSTANT = 1.0D;
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private static final Double FACTOR_CONSTANT = 0.8D;
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@Autowired
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private PersonalVersionManager personalVersionManager;
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@Autowired
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private BigDataRedisService bigDataRedisService;
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private BidataServiceCaller bidataServiceCaller;
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@SearchCacheAble(cacheInMinute = 30, cacheName = "PERSIONAL_VECTOR", returnClass = PersonalizedSearch.class, cacheType = CacheType.SEARCH_REDIS, includeParams = { "uid" })
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public PersonalizedSearch getPersonalizedSearch(Map<String, String> paramMap) {
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@@ -43,7 +37,7 @@ public class PersonalVectorFeatureSearch { |
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return null;
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}
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// 3. 获取用户的特征向量
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String userVectorFeature = bigDataRedisService.getUserVectorFeature(uid, vectorFeatureVersion);
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String userVectorFeature = bidataServiceCaller.getUserVectorFeature(uid, vectorFeatureVersion);
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if (StringUtils.isEmpty(userVectorFeature)) {
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return null;
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}
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@@ -54,98 +48,4 @@ public class PersonalVectorFeatureSearch { |
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}
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}
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/**
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* 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
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*
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* @param uid
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* 用户标识
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* @param productVectorFeatureMap
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* 商品特征map
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* @return 相关性计算结果
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*/
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public Map<String, Object> calVectorFeature(String uid, String version, Map<String, String> productVectorFeatureMap) {
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Map<String, Object> scoreMap = new HashMap<>();
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if (productVectorFeatureMap == null || productVectorFeatureMap.isEmpty()) {
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scoreMap.put("productVectorFeatureMap", "empty");
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return scoreMap;
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}
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String vectorFeatureVersion = StringUtils.isNotEmpty(version) ? version : personalVersionManager.getCurrentVersionInZk();
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scoreMap.put("vectorFeatureVersion", vectorFeatureVersion);
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if (StringUtils.isEmpty(vectorFeatureVersion) || "-1".equals(vectorFeatureVersion)) {
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return scoreMap;
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}
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// 2. 获取用户的特征向量
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String userVectorFeature = bigDataRedisService.getUserVectorFeature(uid, vectorFeatureVersion);
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scoreMap.put("userVectorFeature", userVectorFeature);
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if (StringUtils.isEmpty(userVectorFeature)) {
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return scoreMap;
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}
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String[] userFeatureFactorArr = userVectorFeature.split(",");
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double tempUserFeatureVectorNorm = 0.0D;
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int dimensionOfFactors = userFeatureFactorArr.length;
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double[] userFeatureFactors = new double[dimensionOfFactors];
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double temp;
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for (int index = 0; index < dimensionOfFactors; index++) {
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temp = Double.parseDouble(userFeatureFactorArr[index].trim());
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userFeatureFactors[index] = temp;
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tempUserFeatureVectorNorm += temp * temp;
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}
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final double userFeatureVectorNorm = tempUserFeatureVectorNorm;
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scoreMap.put("userFeatureVectorNorm", userFeatureVectorNorm);
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scoreMap.put("dimensionOfFactors", dimensionOfFactors);
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// 3. 计算相关性得分
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productVectorFeatureMap.forEach((skn, vector) -> {
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Map<String, Object> content = new HashMap<String, Object>();
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content.put("vector", vector);
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content.put("score", calculateScore(vector, vectorFeatureVersion, userFeatureFactors, userFeatureVectorNorm));
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scoreMap.put(skn, content);
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});
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return scoreMap;
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}
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public double calculateScore(String productFeatureFactor, String vectorFeatureVersion, double[] userFeatureFactors, double userFeatureVectorNorm) {
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if (productFeatureFactor == null || productFeatureFactor.trim().isEmpty()) {
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return BASE_CONSTANT;
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}
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String versionPrefix = vectorFeatureVersion + "|";
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if (!productFeatureFactor.trim().startsWith(versionPrefix)) {
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return BASE_CONSTANT;
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}
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String[] productFeatureFactorArr = productFeatureFactor.trim().substring(versionPrefix.length()).split(",");
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if (productFeatureFactorArr == null || productFeatureFactorArr.length != userFeatureFactors.length) {
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return BASE_CONSTANT;
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}
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double prodFeatureVectorNorm = 0.0D;
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double productiveSum = 0.0D;
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double tempProdFactor;
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for (int i = 0; i < userFeatureFactors.length; i++) {
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tempProdFactor = Double.parseDouble(productFeatureFactorArr[i].trim());
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productiveSum += tempProdFactor * userFeatureFactors[i];
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prodFeatureVectorNorm += tempProdFactor * tempProdFactor;
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}
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if (prodFeatureVectorNorm == 0) {
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return BASE_CONSTANT;
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}
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double cosScore = productiveSum / (Math.sqrt(prodFeatureVectorNorm) * Math.sqrt(userFeatureVectorNorm));
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double finalScore = BASE_CONSTANT + FACTOR_CONSTANT * cosScore;
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return finalScore;
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}
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public static void main(String[] args) {
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Calendar now = Calendar.getInstance();
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now.add(Calendar.YEAR, -1);
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System.out.println(DateUtil.getFirstTimeSecond(now.getTime()));
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}
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} |
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