rank module: vov predict model, add params

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

@@ -32,8 +32,6 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
     @ApolloJsonValue("${rank.score.merge.weightv562:}")
     private Map<String, Double> mergeWeight;
 
-    @ApolloJsonValue("${video.vov_model.weightv1:}")
-    private Map<String, Double> vovWeight;
 
 
 
@@ -342,25 +340,31 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
 
         // 获取VoV预测模型参数
         // 融合权重
-        double alpha_vov = vovWeight.getOrDefault("alpha_vov", 1.0);
+        double alpha_vov = mergeWeight.getOrDefault("alpha_vov", 1.0);
 
-        double vov_thresh = vovWeight.getOrDefault("vov_thresh", 0.1);
+        double vov_thresh = mergeWeight.getOrDefault("vov_thresh", 0.1);
 
-        double view_thresh = vovWeight.getOrDefault("view_thresh", 1535.0);
+        double view_thresh = mergeWeight.getOrDefault("view_thresh", 1535.0);
 
-        List<Double> weightList = new ArrayList<>();
-        weightList.add(vovWeight.getOrDefault("d2_ago_vov_w", 0.0));
-        weightList.add(vovWeight.getOrDefault("d1_ago_vov_w", 0.0));
-        weightList.add(vovWeight.getOrDefault("h48_ago_vov_w", 0.0));
-        weightList.add(vovWeight.getOrDefault("h24_ago_vov_w", 0.0));
-        weightList.add(vovWeight.getOrDefault("h3_ago_vov_w", 0.0));
-        weightList.add(vovWeight.getOrDefault("h2_ago_vov_w", 0.0));
-        weightList.add(vovWeight.getOrDefault("h1_ago_vov_w", 0.0));
+        double level50_vov = mergeWeight.getOrDefault("level50_vov", 0.123);
+
+        double level_95_vov = mergeWeight.getOrDefault("level_95_vov", 0.178);
 
+        double beta_vov = mergeWeight.getOrDefault("beta_vov", 100.0);
+
+        List<Double> weightList = new ArrayList<>();
+        weightList.add(mergeWeight.getOrDefault("d2_ago_vov_w", 0.0));
+        weightList.add(mergeWeight.getOrDefault("d1_ago_vov_w", 0.0));
+        weightList.add(mergeWeight.getOrDefault("h48_ago_vov_w", 0.0));
+        weightList.add(mergeWeight.getOrDefault("h24_ago_vov_w", 0.0));
+        weightList.add(mergeWeight.getOrDefault("h3_ago_vov_w", 0.0));
+        weightList.add(mergeWeight.getOrDefault("h2_ago_vov_w", 0.0));
+        weightList.add(mergeWeight.getOrDefault("h1_ago_vov_w", 0.0));
 
 
 
-        Map<String, Map<String, String>> vid2VovFeatureMap = this.getVideoRedisFeature(vids, "redis:vid_vovhour4rank::");
+        Map<String, Map<String, String>> vid2MapFeature = this.getVideoRedisFeature(vids, "redis:vid_hasreturn_rov:");
+        Map<String, Map<String, String>> vid2VovFeatureMap = this.getVideoRedisFeature(vids, "redis:vid_vovhour4rank:");
         List<Video> result = new ArrayList<>();
 //        String hasReturnRovKey = mergeWeight.getOrDefault("hasReturnRovKey", 1.0) < 0.5 ? "rate_1" : "rate_n";
 //        Double chooseFunction = mergeWeight.getOrDefault("chooseFunction", 0.0);
@@ -416,8 +420,14 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
             featureList.add(h1_ago_vov);
 
             // todo 线性加权 预测VoV
-            double vov_p = calculateScore(featureList, weightList, vov_thresh, view_thresh, h1_ago_view);
 
+
+            double vov_p = calculateScore(featureList, weightList, vov_thresh, view_thresh, h1_ago_view,level50_vov,level_95_vov,beta_vov);
+
+
+            double hasReturnRovScore = Double.parseDouble(vid2MapFeature.getOrDefault(item.getVideoId() + "", new HashMap<>())
+                    .getOrDefault("rate_n", "0"));
+            item.getScoresMap().put("hasReturnRovScore", hasReturnRovScore);
             score = fmRov  * (1.0 + alpha_vov * vov_p);
 
 
@@ -444,8 +454,9 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
         return result;
     }
 
+
     private  double calculateScore(List<Double> featureList, List<Double> weightList,
-                                        double vov_thresh, double view_thresh, double h1_ago_view) {
+                                        double vov_thresh, double view_thresh, double h1_ago_view,double level50_vov,double level_95_vov,double beta_vov) {
         // 检查 h1_ago_view 条件
         if (h1_ago_view == -2 || h1_ago_view == -1 || h1_ago_view < view_thresh) {
             return 0;
@@ -477,11 +488,11 @@ public class RankStrategy4RegionMergeModelV562 extends RankStrategy4RegionMergeM
             score += featureList.get(index) * weight;
         }
         // 调整vov
-        if (score < 0.1) {
+        if (score < vov_thresh) {
             score = 0;
         } else {
-            double term1 = 1 / (1 + Math.exp(-100 * (score - 0.123)));
-            double term2 = 1 + Math.exp(-100 * (0.178 - 0.123));
+            double term1 = 1 / (1 + Math.exp(-1*beta_vov * (score - level50_vov)));
+            double term2 = 1 + Math.exp(-1*beta_vov * (level_95_vov - level50_vov));
             score = term1 * term2;
         }
         return score;