需求召回视频 相似度权重可配置 默认0.2

core/src/main/java/com/tzld/videoVector/job/ChannelDemandMatchJob.java

@@ -122,6 +122,12 @@ public class ChannelDemandMatchJob {
     @Value("${channel.demand.dimension-stat.min-uv-ratio:0.002}")
     private double dimensionStatMinUvRatio;
 
+    /**
+     * 向量召回综合评分相关性权重 alpha，取值 0~1
+     */
+    @Value("${channel.demand.recall.alpha:0.2}")
+    private double recallAlpha;
+
     /**
      * 渠道维度过滤配置：按渠道+维度指定topN过滤比例
      * 格式: {"渠道A": {"增长的头部": 0.25, "传播的头部": 0.3}}
@@ -503,6 +509,7 @@ public class ChannelDemandMatchJob {
         param.setQueryText(queryText);
         param.setConfigCode(configCode);
         param.setTopN(topN);
+        param.setAlpha(recallAlpha);
 
         RecallVideoScoreVO scoreVO = getRecallResultWithCache(param);
         if (scoreVO == null || CollectionUtils.isEmpty(scoreVO.getItems())) {
@@ -720,7 +727,7 @@ public class ChannelDemandMatchJob {
 
                 // 计算综合评分：sim默认1，rov取totalRov
                 double rov = result.getMatchRov() != null ? result.getMatchRov() : 0.0;
-                result.setMatchScore(VectorUtils.calculateScore(1.0, rov));
+                result.setMatchScore(VectorUtils.calculateScore(1.0, rov, recallAlpha));
                 result.setMatchSim(1.0);
                 result.setMatchRov(rov);
 

core/src/main/java/com/tzld/videoVector/util/VectorUtils.java

@@ -67,8 +67,8 @@ public final class VectorUtils {
      * @param rov ROV值
      * @return 综合评分（保留4位小数）
      */
-    public static double calculateScore(double sim, double rov) {
-        return calculateScore(sim, rov, DEFAULT_ALPHA, DEFAULT_SIM_MIN, DEFAULT_SIM_MAX, DEFAULT_ROV_P5, DEFAULT_ROV_P95);
+    public static double calculateScore(double sim, double rov, double alpha) {
+        return calculateScore(sim, rov, alpha, DEFAULT_SIM_MIN, DEFAULT_SIM_MAX, DEFAULT_ROV_P5, DEFAULT_ROV_P95);
     }
 
     /**