feat:修改校准方式

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV562.java

@@ -2,6 +2,7 @@ package com.tzld.piaoquan.recommend.server.service.rank.strategy;
 
 import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
 import com.google.common.collect.Maps;
+import com.google.common.collect.Sets;
 import com.google.common.reflect.TypeToken;
 import com.tzld.piaoquan.recommend.server.common.ThreadPoolFactory;
 import com.tzld.piaoquan.recommend.server.common.base.RankItem;
@@ -37,6 +38,7 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
 
     private Map<Pair<Double, Double>, Pair<Double, Double>> strPlusCalibrationCoefficientMap = Maps.newLinkedHashMap();
 
+    private static final Set<String> HOT_SCENE_TYPE_SET = Sets.newHashSet("1007", "1008");
 
     @Autowired
     private FeatureService featureService;
@@ -342,6 +344,7 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
             double fmRov = this.fmRovRestoreAndCalibration(item);
             item.getScoresMap().put("fmRov", fmRov);
             double hasReturnRovScore = this.calcHasReturnRovScore(vid2MapFeature.getOrDefault(item.getVideoId() + "", new HashMap<>()), param.getHotSceneType().toString(), item);
+            hasReturnRovScore = Math.pow(hasReturnRovScore, 1.1);
             item.getScoresMap().put("hasReturnRovScore", hasReturnRovScore);
             double vor = this.calcVorScore(vid2MapFeature.getOrDefault(item.getVideoId() + "", new HashMap<>()));
             item.getScoresMap().put("vor", vor);
@@ -366,15 +369,20 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
     private double calcHasReturnRovScore(Map<String, String> feature, String hotSceneType, RankItem item) {
 
         try {
-            Map<String, String> hotSceneJson = JSONUtils.fromJson(feature.getOrDefault(hotSceneType, "{}"), new TypeToken<Map<String, String>>() {
-            }, new HashMap<>());
+
+            if (!HOT_SCENE_TYPE_SET.contains(hotSceneType)) {
+                hotSceneType = "other";
+            }
+
             Map<String, String> sumJson = JSONUtils.fromJson(feature.getOrDefault("sum", "{}"), new TypeToken<Map<String, String>>() {
             }, new HashMap<>());
+            Map<String, String> hotSceneJson = JSONUtils.fromJson(feature.getOrDefault(hotSceneType, "{}"), new TypeToken<Map<String, String>>() {
+            }, new HashMap<>());
 
-            String hotSceneHasReturn = hotSceneJson.getOrDefault("hasreturn", "0");
             String sumHasReturn = sumJson.getOrDefault("hasreturn", "0");
+            String hotSceneHasReturn = hotSceneJson.getOrDefault("hasreturn", sumHasReturn);
 
-            item.getScoresMap().put("hotSceneHasReturn", Double.parseDouble(hotSceneHasReturn));
+            item.getScoresMap().put("hotSceneTypeHasReturn", Double.parseDouble(hotSceneHasReturn));
             item.getScoresMap().put("sumHasReturn", Double.parseDouble(sumHasReturn));
 
             return Stream.of(hotSceneHasReturn, sumHasReturn)
@@ -403,6 +411,12 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
         // fmRov采样恢复
         double fmRov = restoreScore(fmRovOrigin);
         item.getScoresMap().put("fmRovRestore", fmRov);
+        if (fmRov < 0) {
+            fmRov = 0;
+        }
+        if (fmRov > 1) {
+            fmRov = 1;
+        }
         try {
             readCalibrationFile();
 

recommend-server-service/src/main/resources/562_calibration_coefficient.txt

@@ -997,4 +997,4 @@
 0.098303,0.108875,0.7897,0.7878
 0.108876,0.124901,0.7657,0.7354
 0.124901,0.155439,0.7105,0.7139
-0.15544,1,0.5894,0.6886
+0.15544,1.001,0.5894,0.6886