流量池尾号0同步汤姆森策略。

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/recall/RecallService.java

@@ -224,6 +224,7 @@ public class RecallService implements ApplicationContextAware {
                     }
                 } else if (param.getFlowPoolAbtestGroup().equals(FlowPoolConstants.EXPERIMENTAL_FLOW_SET_LEVEL_SCORE)) {
                     strategies.add(strategyMap.get(QuickFlowPoolWithLevelScoreRecallStrategy.class.getSimpleName()));
+                    // 在执行中
                     strategies.add(strategyMap.get(FlowPoolWithLevelScoreRecallStrategy.class.getSimpleName()));
                 } else {
                     strategies.add(strategyMap.get(QuickFlowPoolWithScoreRecallStrategy.class.getSimpleName()));

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/recall/strategy/FlowPoolWithLevelScoreRecallStrategy.java

@@ -1,18 +1,31 @@
 package com.tzld.piaoquan.recommend.server.service.recall.strategy;
 
+import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
+import com.google.common.collect.Lists;
+import com.tzld.piaoquan.recommend.server.model.Video;
+import com.tzld.piaoquan.recommend.server.service.filter.FilterParam;
+import com.tzld.piaoquan.recommend.server.service.filter.FilterResult;
 import com.tzld.piaoquan.recommend.server.service.flowpool.FlowPoolConfigService;
 import com.tzld.piaoquan.recommend.server.service.flowpool.FlowPoolConstants;
+import com.tzld.piaoquan.recommend.server.service.recall.FilterParamFactory;
 import com.tzld.piaoquan.recommend.server.service.recall.RecallParam;
+import com.tzld.piaoquan.recommend.server.service.score.ScorerUtils;
+import com.tzld.piaoquan.recommend.server.service.score4recall.ScorerPipeline4Recall;
+import com.tzld.piaoquan.recommend.server.util.JSONUtils;
 import lombok.Data;
+import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.collections4.CollectionUtils;
 import org.apache.commons.lang3.RandomUtils;
+import org.apache.commons.lang3.math.NumberUtils;
 import org.apache.commons.lang3.tuple.Pair;
 import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.data.redis.core.ZSetOperations;
 import org.springframework.stereotype.Service;
 
 import java.math.BigDecimal;
 import java.math.RoundingMode;
 import java.util.*;
+import java.util.stream.Collectors;
 
 import static com.tzld.piaoquan.recommend.server.service.flowpool.FlowPoolConstants.KEY_WITH_LEVEL_SCORE_FORMAT;
 
@@ -21,7 +34,8 @@ import static com.tzld.piaoquan.recommend.server.service.flowpool.FlowPoolConsta
  */
 @Service
 public class FlowPoolWithLevelScoreRecallStrategy extends AbstractFlowPoolWithLevelScoreRecallStrategy {
-
+    @ApolloJsonValue("${ifOneLevelRandom:true}")
+    private boolean ifOneLevelRandom;
     @Autowired
     private FlowPoolConfigService flowPoolConfigService;
 
@@ -29,11 +43,12 @@ public class FlowPoolWithLevelScoreRecallStrategy extends AbstractFlowPoolWithLe
     Pair<String, String> flowPoolKeyAndLevel(RecallParam param) {
         //# 1. 获取流量池各层级分发概率权重
         Map<String, Double> levelWeightMap = flowPoolConfigService.getLevelWeight();
+
         // 2. 判断各层级是否有视频需分发
         List<LevelWeight> availableLevels = new ArrayList<>();
         for (Map.Entry<String, Double> entry : levelWeightMap.entrySet()) {
             String levelKey = String.format(KEY_WITH_LEVEL_SCORE_FORMAT, param.getAppType(), entry.getKey());
-            if (redisTemplate.hasKey(levelKey)) {
+            if (Boolean.TRUE.equals(redisTemplate.hasKey(levelKey))) {
                 LevelWeight lw = new LevelWeight();
                 lw.setLevel(entry.getKey());
                 lw.setLevelKey(levelKey);
@@ -41,6 +56,8 @@ public class FlowPoolWithLevelScoreRecallStrategy extends AbstractFlowPoolWithLe
                 availableLevels.add(lw);
             }
         }
+
+        //log.info("availableLevels {}", JSONUtils.toJson(availableLevels));
         if (CollectionUtils.isEmpty(availableLevels)) {
             return Pair.of("", "");
         }
@@ -52,7 +69,7 @@ public class FlowPoolWithLevelScoreRecallStrategy extends AbstractFlowPoolWithLe
         BigDecimal weightSumBD = new BigDecimal(weightSum);
         double level_p_low = 0;
         double weight_temp = 0;
-        double level_p_up;
+        double level_p_up = 0;
         Map<String, LevelP> level_p_mapping = new HashMap<>();
         for (LevelWeight lw : availableLevels) {
             BigDecimal bd = new BigDecimal(weight_temp + lw.getWeight());
@@ -68,7 +85,6 @@ public class FlowPoolWithLevelScoreRecallStrategy extends AbstractFlowPoolWithLe
         }
 
         // 4. 随机生成[0,1)之间数，返回相应概率区间的key
-
         double random_p = RandomUtils.nextDouble(0, 1);
         for (Map.Entry<String, LevelP> entry : level_p_mapping.entrySet()) {
             if (random_p >= entry.getValue().getMin()
@@ -97,4 +113,68 @@ public class FlowPoolWithLevelScoreRecallStrategy extends AbstractFlowPoolWithLe
     public String pushFrom() {
         return FlowPoolConstants.PUSH_FORM;
     }
+
+    @Override
+    public List<Video> recall(RecallParam param) {
+        Pair<String, String> flowPoolKeyAndLevel = flowPoolKeyAndLevel(param);
+        String flowPoolKey = flowPoolKeyAndLevel.getLeft();
+        String level = flowPoolKeyAndLevel.getRight();
+        Set<ZSetOperations.TypedTuple<String>> data = redisTemplate.opsForZSet().reverseRangeWithScores(flowPoolKey, 0, 1000);
+        if (CollectionUtils.isEmpty(data)) {
+            return null;
+        }
+        Map<String, String> videoFlowPoolMap = new LinkedHashMap<>();
+        Map<Long, String> videoFlowPoolMap_ = new LinkedHashMap<>();
+        for (ZSetOperations.TypedTuple<String> value : data) {
+            String[] values = Objects.requireNonNull(value.getValue()).split("-");
+            videoFlowPoolMap.put(values[0], values[1]);
+            videoFlowPoolMap_.put(NumberUtils.toLong(values[0], 0),  values[1]);
+        }
+        Map<Long, Double> resultmap = null;
+        if ("1".equals(level) && ifOneLevelRandom) {
+            // 流量池一层改为全随机
+            int limitSize = 60;
+            List<Long> keyList = new ArrayList<>(videoFlowPoolMap_.keySet());
+            Collections.shuffle(keyList);
+            resultmap = keyList.stream().limit(limitSize).collect(Collectors.toMap(
+                    key -> key,
+                    key -> Math.random()
+            ));
+        } else {
+            ScorerPipeline4Recall pipeline = ScorerUtils.getScorerPipeline4Recall("feeds_recall_config_tomson.conf");
+            List<List<Pair<Long, Double>>> results = pipeline.recall(videoFlowPoolMap);
+            List<Pair<Long, Double>> result = results.get(0);
+            resultmap = result.stream()
+                    .collect(Collectors.toMap(
+                            Pair::getLeft, // 键是Pair的left值
+                            Pair::getRight, // 值是Pair的right值
+                            (existingValue, newValue) -> existingValue, // 如果键冲突，选择保留现有的值（或者你可以根据需要定义其他合并策略）
+                            LinkedHashMap::new // 使用LinkedHashMap来保持插入顺序（如果需要的话）
+                    ));
+        }
+
+        // 3 召回内部过滤
+        FilterParam filterParam = FilterParamFactory.create(param, new ArrayList<>(resultmap.keySet()));
+        filterParam.setForceTruncation(10000);
+        filterParam.setConcurrent(true);
+        filterParam.setNotUsePreView(false);
+        FilterResult filterResult = filterService.filter(filterParam);
+        List<Video> videosResult = new ArrayList<>();
+        if (filterResult != null && CollectionUtils.isNotEmpty(filterResult.getVideoIds())) {
+            Map<Long, Double> finalResultmap = resultmap;
+            filterResult.getVideoIds().forEach(vid -> {
+                Video recallData = new Video();
+                recallData.setVideoId(vid);
+                recallData.setAbCode(param.getAbCode());
+                recallData.setRovScore(finalResultmap.getOrDefault(vid, 0.0));
+                recallData.setPushFrom(pushFrom());
+                recallData.setFlowPool(videoFlowPoolMap_.get(vid));
+                recallData.setFlowPoolAbtestGroup(param.getFlowPoolAbtestGroup());
+                recallData.setLevel(level);
+                videosResult.add(recallData);
+            });
+        }
+        videosResult.sort(Comparator.comparingDouble(o -> -o.getRovScore()));
+        return videosResult;
+    }
 }