向量缓存在本地

@@ -83,12 +83,10 @@ public class RecallResultBuilder {
         paramMap.put("uid",""+uid);
         PersonalizedSearch personalizedSearch = personalVectorFeatureSearch.getPersonalizedSearch(paramMap);
         UserFeatureFactor userFeatureFactor = new UserFeatureFactor(personalizedSearch);
-        RECALL_NEW_LOGGER.info("userFeatureFactor info ,factor is[{}], version is[{}] ",userFeatureFactor.userFeatureFactors,userFeatureFactor.vectorFeatureVersion);
         //2、计算相关性
         for (RecallMergerResult.SknResult sknResult : sknResultList) {
             double score = productFeatureFactorHepler.calProductFeatureFactor(userFeatureFactor, sknResult.getFactor());
             sknResult.setScore(score);
-            RECALL_NEW_LOGGER.info("skn is [{}], factor is[{}] ,score is [{}] ",sknResult.getProductSkn(),sknResult.getFactor(),score);
         }
         //3、按得分排序
         Collections.sort(sknResultList, new Comparator<RecallMergerResult.SknResult>() {

@@ -54,7 +54,7 @@ public class QueryProductVectorInfoCacheBean extends AbstractCacheBean<SknVector
 
     @Override
     protected boolean useEhCache() {
-        return false;
+        return true;
     }
 
     @Override