ADD：实验逻辑控制

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/framework/score/ScorerUtils.java

@@ -19,15 +19,12 @@ public final class ScorerUtils {
 
     private static final Map<String, ScorerPipeline> scorerPipelineCache = new ConcurrentHashMap<>();
 
-    public static String BASE_CONF_NEW_FEED = "feeds_score_config_new_baseline.conf";
-
-    public static String FLOWPOOL_CONF = "feeds_score_config_thompson.conf";
+    public static String BASE_CONF_FEED = "feeds_score_config_new_baseline.conf";
 
 
     public static void warmUp() {
         log.info("scorer warm up ");
-        ScorerUtils.init(BASE_CONF_NEW_FEED);
-        ScorerUtils.init(FLOWPOOL_CONF);
+        ScorerUtils.init(BASE_CONF_FEED);
     }
 
     private ScorerUtils() {

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/implement/TopRecommendPipeline.java

@@ -454,7 +454,7 @@ public class TopRecommendPipeline {
         log.info("ItemFeature = {}", JSONUtils.toJson(videoFeatures));
 
 
-        List<RankItem> rovRecallScore = ScorerUtils.getScorerPipeline(ScorerUtils.BASE_CONF_NEW_FEED)
+        List<RankItem> rovRecallScore = ScorerUtils.getScorerPipeline(ScorerUtils.BASE_CONF_FEED)
                 .scoring(sceneFeatureMap, userFeatureMap, rankItems);
         log.info("mergeAndRankRovRecallNew rovRecallScore={}", JSONUtils.toJson(rovRecallScore));
         JSONObject obj = new JSONObject();

recommend-server-service/src/main/resources/feeds_score_config_thompson_new.conf

@@ -0,0 +1,7 @@
+scorer-config = {
+  flowpool-score-config = {
+    scorer-name = "com.tzld.piaoquan.recommend.server.implement.score.VlogThompsonScorer"
+    scorer-priority = 99
+    model-path = "video_thompson_model/model_video_thompson.txt"
+  }
+}