Merge branch 'feature/zhangbo_model' of algorithm/recommend-server into master

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelBasic.java

@@ -36,7 +36,7 @@ public class RankStrategy4RegionMergeModelBasic extends RankService {
     private Map<String, Double> mergeWeight;
     @ApolloJsonValue("${RankStrategy4DensityFilterV2:}")
     private Map<String, Map<String, Map<String, String>>> filterRules = new HashMap<>();
-    final private String CLASS_NAME = this.getClass().getSimpleName();
+    String CLASS_NAME = this.getClass().getSimpleName();
 
     public void duplicate(Set<Long> setVideo, List<Video> videos) {
         Iterator<Video> iterator = videos.iterator();
@@ -322,6 +322,35 @@ public class RankStrategy4RegionMergeModelBasic extends RankService {
         return result;
     }
 
+    static class Tuple4 {
+        public Map<String, String> first;
+        public Map<String, String> second;
+        public Map<String, String> third;
+
+        public String name;
+
+        public Tuple4(Map<String, String> first, Map<String, String> second, Map<String, String> third, String name) {
+            this.first = first;
+            this.second = second;
+            this.third = third;
+            this.name = name;
+        }
+
+    }
+
+    static class Tuple2 {
+        public Map<String, String> first;
+
+        public String name;
+
+        public Tuple2(Map<String, String> first, String name) {
+            this.first = first;
+            this.name = name;
+        }
+
+    }
+
+
     public static void main(String[] args) {
 
     }

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV563.java

@@ -4,60 +4,38 @@ import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
 import com.google.common.reflect.TypeToken;
 import com.tzld.piaoquan.recommend.server.common.base.RankItem;
 import com.tzld.piaoquan.recommend.server.model.Video;
+import com.tzld.piaoquan.recommend.server.service.FeatureService;
 import com.tzld.piaoquan.recommend.server.service.rank.RankParam;
-import com.tzld.piaoquan.recommend.server.service.rank.RankResult;
-import com.tzld.piaoquan.recommend.server.service.rank.RankService;
 import com.tzld.piaoquan.recommend.server.service.rank.extractor.ExtractorUtils;
-import com.tzld.piaoquan.recommend.server.service.rank.extractor.RankExtractorItemFeature;
-import com.tzld.piaoquan.recommend.server.service.rank.extractor.RankExtractorItemTags;
-import com.tzld.piaoquan.recommend.server.service.rank.extractor.RankExtractorUserFeature;
-import com.tzld.piaoquan.recommend.server.service.rank.processor.RankProcessorBoost;
-import com.tzld.piaoquan.recommend.server.service.rank.processor.RankProcessorDensity;
-import com.tzld.piaoquan.recommend.server.service.rank.processor.RankProcessorInsert;
-import com.tzld.piaoquan.recommend.server.service.rank.processor.RankProcessorTagFilter;
 import com.tzld.piaoquan.recommend.server.service.recall.strategy.*;
 import com.tzld.piaoquan.recommend.server.service.score.ScorerUtils;
 import com.tzld.piaoquan.recommend.server.util.CommonCollectionUtils;
 import com.tzld.piaoquan.recommend.server.util.JSONUtils;
 import lombok.extern.slf4j.Slf4j;
-import org.apache.commons.collections4.CollectionUtils;
-import org.apache.commons.lang3.RandomUtils;
-import org.springframework.data.redis.connection.RedisConnectionFactory;
-import org.springframework.data.redis.connection.RedisStandaloneConfiguration;
-import org.springframework.data.redis.connection.jedis.JedisConnectionFactory;
-import org.springframework.data.redis.core.RedisTemplate;
-import org.springframework.data.redis.serializer.StringRedisSerializer;
+import org.apache.commons.collections4.MapUtils;
+import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.stereotype.Service;
 
-import java.text.SimpleDateFormat;
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
 import java.util.*;
 import java.util.stream.Collectors;
 
-/**
- * @author zhangbo
- * @desc 地域召回融合 流量池汤姆森
- */
 @Service
 @Slf4j
-public class RankStrategy4RegionMergeModelV563 extends RankService {
+public class RankStrategy4RegionMergeModelV563 extends RankStrategy4RegionMergeModelBasic {
     @ApolloJsonValue("${rank.score.merge.weightv563:}")
     private Map<String, Double> mergeWeight;
-    @ApolloJsonValue("${RankStrategy4DensityFilterV2:}")
-    private Map<String, Map<String, Map<String, String>>> filterRules = new HashMap<>();
-    final private String CLASS_NAME = this.getClass().getSimpleName();
-
-    public void duplicate(Set<Long> setVideo, List<Video> videos) {
-        Iterator<Video> iterator = videos.iterator();
-        while (iterator.hasNext()) {
-            Video v = iterator.next();
-            if (setVideo.contains(v.getVideoId())) {
-                iterator.remove();
-            } else {
-                setVideo.add(v.getVideoId());
-            }
-        }
-    }
 
+    @Autowired
+    private FeatureService featureService;
+
+    Map<String, double[]> bucketsMap = new HashMap<>();
+    Map<String, Double> bucketsLen = new HashMap<>();
+
+    String CLASS_NAME = this.getClass().getSimpleName();
     @Override
     public List<Video> mergeAndRankRovRecall(RankParam param) {
         Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : new HashMap<>(0);
@@ -72,620 +50,381 @@ public class RankStrategy4RegionMergeModelV563 extends RankService {
         oldRovs.addAll(extractAndSort(param, Region24HRecallStrategy.PUSH_FORM));
         oldRovs.addAll(extractAndSort(param, RegionRelative24HRecallStrategy.PUSH_FORM));
         oldRovs.addAll(extractAndSort(param, RegionRelative24HDupRecallStrategy.PUSH_FORM));
-        int sizeReturn = param.getSize();
         removeDuplicate(oldRovs);
+        int sizeReturn = param.getSize();
         List<Video> v0 = oldRovs.size() <= sizeReturn
                 ? oldRovs
                 : oldRovs.subList(0, sizeReturn);
-
-        //-------------------地域相关召回 融合+去重-------------------
-        List<Video> v1 = extractAndSort(param, RegionRealtimeRecallStrategyV1.PUSH_FORM);
-        List<Video> v2 = extractAndSort(param, RegionRealtimeRecallStrategyV2_sort.PUSH_FORM);
-        List<Video> v3 = extractAndSort(param, RegionRealtimeRecallStrategyV3.PUSH_FORM);
-        List<Video> v4 = extractAndSort(param, RegionRealtimeRecallStrategyV4.PUSH_FORM);
-        //-------------------相关性召回 融合+去重-------------------
-        List<Video> v5 = extractAndSort(param, SimHotVideoRecallStrategy.PUSH_FORM);
-        List<Video> v6 = extractAndSort(param, ReturnVideoRecallStrategy.PUSH_FORM);
-        //-------------------节日扶持召回 融合+去重-------------------
-        List<Video> v7 = extractAndSort(param, FestivalRecallStrategyV1.PUSH_FORM);
-        //-------------------流量池回捞-------------------
-        List<Video> v9 = extractAndSort(param, FlowPoolLastDayTopRecallStrategy.PUSH_FORM);
-
-        // 去重
         Set<Long> setVideo = new HashSet<>();
         this.duplicate(setVideo, v0);
-        this.duplicate(setVideo, v5);
-        this.duplicate(setVideo, v6);
-        this.duplicate(setVideo, v4);
-        this.duplicate(setVideo, v3);
-        this.duplicate(setVideo, v2);
-        this.duplicate(setVideo, v9);
-        this.duplicate(setVideo, v1);
-        this.duplicate(setVideo, v7);
-
-
-
-        List<Video> rovRecallRank = new ArrayList<>();
-        rovRecallRank.addAll(v0);
-        rovRecallRank.addAll(v1.subList(0, Math.min(mergeWeight.getOrDefault("v1", 25.0).intValue(), v1.size())));
-        rovRecallRank.addAll(v2.subList(0, Math.min(mergeWeight.getOrDefault("v2", 15.0).intValue(), v2.size())));
-        rovRecallRank.addAll(v3.subList(0, Math.min(mergeWeight.getOrDefault("v3", 10.0).intValue(), v3.size())));
-        rovRecallRank.addAll(v4.subList(0, Math.min(mergeWeight.getOrDefault("v4", 5.0).intValue(), v4.size())));
-        rovRecallRank.addAll(v5.subList(0, Math.min(mergeWeight.getOrDefault("v5", 5.0).intValue(), v5.size())));
-        rovRecallRank.addAll(v6.subList(0, Math.min(mergeWeight.getOrDefault("v6", 5.0).intValue(), v6.size())));
-        rovRecallRank.addAll(v7.subList(0, Math.min(mergeWeight.getOrDefault("v7", 5.0).intValue(), v7.size())));
-        rovRecallRank.addAll(v9.subList(0, Math.min(mergeWeight.getOrDefault("v9", 5.0).intValue(), v9.size())));
+        setVideo.addAll(v0.stream().map(Video::getVideoId).collect(Collectors.toSet()));
+        List<Video> rovRecallRank = new ArrayList<>(v0);
+        //-------------------return相似召回------------------
+        List<Video> v6 = extractAndSort(param, ReturnVideoRecallStrategy.PUSH_FORM);
+        v6 = v6.stream().filter(r-> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
+        v6 = v6.subList(0, Math.min(mergeWeight.getOrDefault("v6", 5.0).intValue(), v6.size()));
+        rovRecallRank.addAll(v6);
+        setVideo.addAll(v6.stream().map(Video::getVideoId).collect(Collectors.toSet()));
+        //-------------------新地域召回------------------
+        List<Video> v1 = extractAndSort(param, RegionRealtimeRecallStrategyV1.PUSH_FORM);
+        v1 = v1.stream().filter(r-> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
+        v1 = v1.subList(0, Math.min(mergeWeight.getOrDefault("v1", 5.0).intValue(), v1.size()));
+        rovRecallRank.addAll(v1);
+        setVideo.addAll(v1.stream().map(Video::getVideoId).collect(Collectors.toSet()));
+
 
         //-------------------排-------------------
         //-------------------序-------------------
         //-------------------逻-------------------
         //-------------------辑-------------------
 
-        // 1 模型分
-        List<String> rtFeaPart = new ArrayList<>();
-        List<RankItem> items = model(rovRecallRank, param, rtFeaPart);
-        List<String> rtFeaPartKey = new ArrayList<>(Arrays.asList("item_rt_fea_1day_partition", "item_rt_fea_1h_partition"));
-        List<String> rtFeaPartKeyResult = this.redisTemplate.opsForValue().multiGet(rtFeaPartKey);
-        Calendar calendar = Calendar.getInstance();
-        String date = new SimpleDateFormat("yyyyMMdd").format(calendar.getTime());
-        String hour = new SimpleDateFormat("HH").format(calendar.getTime());
-        String rtFeaPart1h = date + hour;
-        if (rtFeaPartKeyResult != null) {
-            if (rtFeaPartKeyResult.get(1) != null) {
-                rtFeaPart1h = rtFeaPartKeyResult.get(1);
-            }
-        }
-        // 2 统计分
-        String cur = rtFeaPart1h;
-        List<String> datehours = new LinkedList<>(); // 时间是倒叙的
-        for (int i = 0; i < 24; ++i) {
-            datehours.add(cur);
-            cur = ExtractorUtils.subtractHours(cur, 1);
+        // TODO 1 批量获取特征  省份参数要对齐  headvid  要传递过来！
+        List<String> vids = CommonCollectionUtils.toListDistinct(rovRecallRank, v -> String.valueOf(v.getVideoId()));
+
+        // k1:视频、k2:表、k3:特征、v:特征值
+        String provinceCn = param.getProvince().replaceAll("省$", "");
+        String headVid = String.valueOf(param.getHeadVid());
+        FeatureService.Feature feature = featureService.getFeature(param.getMid(), vids,
+                String.valueOf(param.getAppType()), provinceCn, headVid);
+        Map<String, Map<String, String>> featureOriginUser = feature.getUserFeature();
+        Map<String, Map<String, Map<String, String>>> featureOriginVideo = feature.getVideoFeature();
+
+
+        // TODO 2 特征处理
+        Map<String, Double> userFeatureMapDouble = new HashMap<>();
+        String mid = param.getMid();
+        Map<String, String> c1 = featureOriginUser.getOrDefault("alg_mid_feature_play", new HashMap<>());
+        Map<String, String> c2 = featureOriginUser.getOrDefault("alg_mid_feature_share_and_return", new HashMap<>());
+        Map<String, String> c3 = featureOriginUser.getOrDefault("alg_mid_feature_play_tags", new HashMap<>());
+        Map<String, String> c4 = featureOriginUser.getOrDefault("alg_mid_feature_return_tags", new HashMap<>());
+        Map<String, String> c5 = featureOriginUser.getOrDefault("alg_mid_feature_share_tags", new HashMap<>());
+        Map<String, String> c6 = featureOriginUser.getOrDefault("alg_mid_feature_feed_exp_share_tags", new HashMap<>());
+        Map<String, String> c7 = featureOriginUser.getOrDefault("alg_mid_feature_feed_exp_return_tags", new HashMap<>());
+        Map<String, String> c8 = featureOriginUser.getOrDefault("alg_mid_feature_sharecf", new HashMap<>());
+        Map<String, String> c9 = featureOriginUser.getOrDefault("alg_mid_feature_returncf", new HashMap<>());
+
+        if (!c1.isEmpty()) {
+            userFeatureMapDouble.put("playcnt_6h", Double.parseDouble(c1.getOrDefault("playcnt_6h", "0")));
+            userFeatureMapDouble.put("playcnt_1d", Double.parseDouble(c1.getOrDefault("playcnt_1d", "0")));
+            userFeatureMapDouble.put("playcnt_3d", Double.parseDouble(c1.getOrDefault("playcnt_3d", "0")));
+            userFeatureMapDouble.put("playcnt_7d", Double.parseDouble(c1.getOrDefault("playcnt_7d", "0")));
         }
-        List<String> datehoursRoot = new LinkedList<>();
-        for (int i = 0; i < 24; ++i) {
-            datehoursRoot.add(String.valueOf(i+1));
+        if (!c2.isEmpty()) {
+            userFeatureMapDouble.put("share_pv_12h", Double.parseDouble(c2.getOrDefault("share_pv_12h", "0")));
+            userFeatureMapDouble.put("share_pv_1d", Double.parseDouble(c2.getOrDefault("share_pv_1d", "0")));
+            userFeatureMapDouble.put("share_pv_3d", Double.parseDouble(c2.getOrDefault("share_pv_3d", "0")));
+            userFeatureMapDouble.put("share_pv_7d", Double.parseDouble(c2.getOrDefault("share_pv_7d", "0")));
+            userFeatureMapDouble.put("return_uv_12h", Double.parseDouble(c2.getOrDefault("return_uv_12h", "0")));
+            userFeatureMapDouble.put("return_uv_1d", Double.parseDouble(c2.getOrDefault("return_uv_1d", "0")));
+            userFeatureMapDouble.put("return_uv_3d", Double.parseDouble(c2.getOrDefault("return_uv_3d", "0")));
+            userFeatureMapDouble.put("return_uv_7d", Double.parseDouble(c2.getOrDefault("return_uv_7d", "0")));
         }
-        // 2.1 item特征提取
-        this.getVideoFeatureFromRedis(items);
 
-
-        for (RankItem item : items) {
-            Map<String, Map<String, Double>> itemRealRootMap = item.getItemRealTimeRootFeature();
-            List<Double> views_20240410 = getStaticData(itemRealRootMap, datehoursRoot, "exp");
-            List<Double> share_20240410 = getStaticData(itemRealRootMap, datehoursRoot, "share");
-            List<Double> return_20240410 = getStaticData(itemRealRootMap, datehoursRoot, "return");
-            List<Double> rov_20240410 = getRateData(return_20240410, views_20240410, 0.0, 0.0);
-            Double rovScore_20240410 = calScoreWeightNoTimeDecay(rov_20240410);
-            List<Double> ros_20240410 = getRateData(return_20240410, share_20240410, 1.0, 10.0);
-            Double rosScore_20240410 = calScoreWeightNoTimeDecay(ros_20240410);
-            item.scoresMap.put("rovScore_20240410", rovScore_20240410);
-            item.scoresMap.put("rosScore_20240410", rosScore_20240410);
-
-            Map<String, Map<String, Double>> itemRealMap = item.getItemRealTimeFeature();
-            List<Double> views = getStaticData(itemRealMap, datehours, "view_pv_list_1h");
-            List<Double> plays = getStaticData(itemRealMap, datehours, "play_pv_list_1h");
-            List<Double> shares = getStaticData(itemRealMap, datehours, "share_pv_list_1h");
-            List<Double> preturns = getStaticData(itemRealMap, datehours, "p_return_uv_list_1h");
-            List<Double> allreturns = getStaticData(itemRealMap, datehours, "return_uv_list_1h");
-
-            List<Double> share2return = getRateData(preturns, shares, 1.0, 1000.0);
-            Double share2returnScore = calScoreWeightNoTimeDecay(share2return);
-            List<Double> view2return = getRateData(preturns, views, 1.0, 1000.0);
-            Double view2returnScore = calScoreWeightNoTimeDecay(view2return);
-            List<Double> view2play = getRateData(plays, views, 1.0, 1000.0);
-            Double view2playScore = calScoreWeightNoTimeDecay(view2play);
-            List<Double> play2share = getRateData(shares, plays, 1.0, 1000.0);
-            Double play2shareScore = calScoreWeightNoTimeDecay(play2share);
-            item.scoresMap.put("share2returnScore", share2returnScore);
-            item.scoresMap.put("view2returnScore", view2returnScore);
-            item.scoresMap.put("view2playScore", view2playScore);
-            item.scoresMap.put("play2shareScore", play2shareScore);
-
-            // 全部回流的rov和ros
-            List<Double> share2allreturn = getRateData(allreturns, shares, 1.0, 10.0);
-            Double share2allreturnScore = calScoreWeightNoTimeDecay(share2allreturn);
-            List<Double> view2allreturn = getRateData(allreturns, views, 0.0, 0.0);
-            Double view2allreturnScore = calScoreWeightNoTimeDecay(view2allreturn);
-            item.scoresMap.put("share2allreturnScore", share2allreturnScore);
-            item.scoresMap.put("view2allreturnScore", view2allreturnScore);
-
-            // 全部回流
-            Double allreturnsScore = calScoreWeightNoTimeDecay(allreturns);
-            item.scoresMap.put("allreturnsScore", allreturnsScore);
-
-            // 平台回流
-            Double preturnsScore = calScoreWeightNoTimeDecay(preturns);
-            item.scoresMap.put("preturnsScore", preturnsScore);
-
-            // rov的趋势
-            double trendScore = calTrendScore(view2return);
-            item.scoresMap.put("trendScore", trendScore);
-
-            // 新视频提取
-            Map<String, String> itemBasicMap = item.getItemBasicFeature();
-            double newVideoScore = calNewVideoScore(itemBasicMap);
-            item.scoresMap.put("newVideoScore", newVideoScore);
-
-        }
-        // 3 融合公式
-        List<Video> result = new ArrayList<>();
-        double a = mergeWeight.getOrDefault("a", 0.1);
-        double b = mergeWeight.getOrDefault("b", 0.0);
-        double c = mergeWeight.getOrDefault("c", 0.000001);
-        double d = mergeWeight.getOrDefault("d", 1.0);
-        double e = mergeWeight.getOrDefault("e", 1.0);
-        double f = mergeWeight.getOrDefault("f", 0.1);
-        double g = mergeWeight.getOrDefault("g", 2.0);
-        double h = mergeWeight.getOrDefault("h", 50.0);
-        double ifAdd = mergeWeight.getOrDefault("ifAdd", 1.0);
-        for (RankItem item : items) {
-            double trendScore = item.scoresMap.getOrDefault("trendScore", 0.0) > 1E-8 ?
-                    item.scoresMap.getOrDefault("trendScore", 0.0) : 0.0;
-            double newVideoScore = item.scoresMap.getOrDefault("newVideoScore", 0.0) > 1E-8 ?
-                    item.scoresMap.getOrDefault("newVideoScore", 0.0) : 0.0;
-            double strScore = item.getScoreStr();
-            double rosScore = item.scoresMap.getOrDefault("share2returnScore", 0.0);
-            double share2allreturnScore = item.scoresMap.getOrDefault("share2allreturnScore", 0.0);
-            double view2allreturnScore = item.scoresMap.getOrDefault("view2allreturnScore", 0.0);
-            double preturnsScore = Math.log(1 + item.scoresMap.getOrDefault("preturnsScore", 0.0));
-            double rovScore_20240410 = item.scoresMap.getOrDefault("rovScore_20240410", 0.0);
-            double rosScore_20240410 = item.scoresMap.getOrDefault("rosScore_20240410", 0.0);
-
-            double score = 0.0;
-            if (ifAdd < 0.5) {
-                score = Math.pow(strScore, a) * Math.pow(rosScore, b) + c * preturnsScore +
-                        (newVideoScore > 1E-8 ? d * trendScore * (e + newVideoScore) : 0.0);
-            } else {
-                score = a * strScore + b * rosScore + c * preturnsScore +
-                        (newVideoScore > 1E-8 ? d * trendScore * (e + newVideoScore) : 0.0);
-
-            }
-            double allreturnsScore = item.scoresMap.getOrDefault("allreturnsScore", 0.0);
-            if (allreturnsScore > h) {
-                score += (f * rosScore_20240410 + g * rovScore_20240410);
+        Map<String, String> c34567Map = new HashMap<>(15);
+        List<Tuple2> tmpList0 = Arrays.asList(
+                new Tuple2(c3, "c3_feature"),
+                new Tuple2(c4, "c4_feature"),
+                new Tuple2(c5, "c5_feature"),
+                new Tuple2(c6, "c6_feature"),
+                new Tuple2(c7, "c7_feature")
+        );
+        for (Tuple2 tuple2 : tmpList0) {
+            for (String key_time : Arrays.asList("tags_1d", "tags_3d", "tags_7d")) {
+                String tags = tuple2.first.getOrDefault(key_time, "");
+                if (!tags.isEmpty()) {
+                    c34567Map.put(tuple2.name + "_" + key_time, tags);
+                }
             }
-            Video video = item.getVideo();
-            video.setScore(score);
-            video.setSortScore(score);
-            video.setScoreStr(item.getScoreStr());
-            video.setScoresMap(item.getScoresMap());
-            result.add(video);
-        }
-        Collections.sort(result, Comparator.comparingDouble(o -> -o.getSortScore()));
-        return result;
-    }
-
-    public double calNewVideoScore(Map<String, String> itemBasicMap) {
-        double existenceDays = Double.valueOf(itemBasicMap.getOrDefault("existence_days", "30"));
-        if (existenceDays > 5) {
-            return 0.0;
-        }
-        double score = 1.0 / (existenceDays + 10.0);
-        return score;
-    }
-
-    public double calTrendScore(List<Double> data) {
-        double sum = 0.0;
-        int size = data.size();
-        for (int i = 0; i < size - 4; ++i) {
-            sum += data.get(i) - data.get(i + 4);
         }
-        if (sum * 10 > 0.6) {
-            sum = 0.6;
-        } else {
-            sum = sum * 10;
-        }
-        if (sum > 0) {
-            // 为了打断点
-            sum = sum;
-        }
-        return sum;
-    }
 
-    public Double calScoreWeightNoTimeDecay(List<Double> data) {
-        Double up = 0.0;
-        Double down = 0.0;
-        for (int i = 0; i < data.size(); ++i) {
-            up += 1.0 * data.get(i);
-            down += 1.0;
-        }
-        return down > 1E-8 ? up / down : 0.0;
-    }
-
-    public List<Double> getRateData(List<Double> ups, List<Double> downs, Double up, Double down) {
-        List<Double> data = new LinkedList<>();
-        for (int i = 0; i < ups.size(); ++i) {
-            if (ExtractorUtils.isDoubleEqualToZero(downs.get(i) + down)) {
-                data.add(0.0);
-            } else {
-                data.add(
-                        (ups.get(i) + up) / (downs.get(i) + down)
-                );
+        Map<String, Map<String, String[]>> c89Map = new HashMap<>(4);
+        List<Tuple2> tmpList1 = Arrays.asList(
+                new Tuple2(c8, "c8_feature"),
+                new Tuple2(c9, "c9_feature")
+        );
+        for (Tuple2 tuple2 : tmpList1) {
+            for (String key_action : Arrays.asList("share", "return")) {
+                String cfListStr = tuple2.first.getOrDefault(key_action, "");
+                if (!cfListStr.isEmpty()) {
+                    Map<String, String[]> cfMap = new HashMap<>();
+                    String[] entries = cfListStr.split(",");
+                    for (String entry : entries) {
+                        String[] rList = entry.split(":");
+                        if (rList.length >= 4) { // 确保分割后有四个元素
+                            String key = rList[0];
+                            String value1 = rList[1];
+                            String value2 = rList[2];
+                            String value3 = rList[3];
+                            String[] strs = {value1, value2, value3};
+                            cfMap.put(key, strs);
+                        }
+                    }
+                    c89Map.put(tuple2.name + "_" + key_action, cfMap);
+                }
             }
         }
-        return data;
-    }
 
-    public List<Double> getStaticData(Map<String, Map<String, Double>> itemRealMap,
-                                      List<String> datehours, String key) {
-        List<Double> views = new LinkedList<>();
-        Map<String, Double> tmp = itemRealMap.getOrDefault(key, new HashMap<>());
-        for (String dh : datehours) {
-            views.add(tmp.getOrDefault(dh, 0.0D) +
-                    (views.isEmpty() ? 0.0 : views.get(views.size() - 1))
-            );
-        }
-        return views;
-    }
 
-    public List<RankItem> model(List<Video> videos, RankParam param,
-                                List<String> rtFeaPart) {
-        List<RankItem> result = new ArrayList<>();
-        if (videos.isEmpty()) {
-            return result;
-        }
+        List<RankItem> rankItems = CommonCollectionUtils.toList(rovRecallRank, RankItem::new);
+        for (RankItem item : rankItems) {
+            Map<String, Double> featureMap = new HashMap<>();
+            String vid = item.getVideoId() + "";
+            Map<String, String> b1 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_all_exp", new HashMap<>());
+            Map<String, String> b2 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_all_share", new HashMap<>());
+            Map<String, String> b3 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_all_return", new HashMap<>());
+            Map<String, String> b6 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_exp2share", new HashMap<>());
+            Map<String, String> b7 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_share2return", new HashMap<>());
+
+            Map<String, String> b8 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_noflow_exp", new HashMap<>());
+            Map<String, String> b9 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_noflow_root_share", new HashMap<>());
+            Map<String, String> b10 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_noflow_root_return", new HashMap<>());
+            Map<String, String> b11 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_flow_exp", new HashMap<>());
+            Map<String, String> b12 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_flow_root_share", new HashMap<>());
+            Map<String, String> b13 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_flow_root_return", new HashMap<>());
+            Map<String, String> b17 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_province_exp", new HashMap<>());
+            Map<String, String> b18 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_province_root_share", new HashMap<>());
+            Map<String, String> b19 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_province_root_return", new HashMap<>());
+
+            List<Tuple4> originData = Arrays.asList(
+                    new Tuple4(b1, b2, b3, "b123"),
+                    new Tuple4(b1, b6, b7, "b167"),
+                    new Tuple4(b8, b9, b10, "b8910"),
+                    new Tuple4(b11, b12, b13, "b111213"),
+                    new Tuple4(b17, b18, b19, "b171819")
+            );
 
-        RedisStandaloneConfiguration redisSC = new RedisStandaloneConfiguration();
-        redisSC.setPort(6379);
-        redisSC.setPassword("Wqsd@2019");
-        redisSC.setHostName("r-bp1pi8wyv6lzvgjy5z.redis.rds.aliyuncs.com");
-        RedisConnectionFactory connectionFactory = new JedisConnectionFactory(redisSC);
-        RedisTemplate<String, String> redisTemplate = new RedisTemplate<>();
-        redisTemplate.setConnectionFactory(connectionFactory);
-        redisTemplate.setDefaultSerializer(new StringRedisSerializer());
-        redisTemplate.afterPropertiesSet();
-
-        // 0: 场景特征处理
-        Map<String, String> sceneFeatureMap = this.getSceneFeature(param);
-
-        // 1: user特征处理
-        Map<String, String> userFeatureMap = new HashMap<>();
-        if (param.getMid() != null && !param.getMid().isEmpty()) {
-            String midKey = "user_info_4video_" + param.getMid();
-            String userFeatureStr = redisTemplate.opsForValue().get(midKey);
-            if (userFeatureStr != null) {
-                try {
-                    userFeatureMap = JSONUtils.fromJson(userFeatureStr,
-                            new TypeToken<Map<String, String>>() {
-                            },
-                            userFeatureMap);
-                } catch (Exception e) {
-                    log.error(String.format("parse user json is wrong in {} with {}", this.CLASS_NAME, e));
+            for (Tuple4 tuple4 : originData) {
+                for (String prefix2 : Arrays.asList("1h", "2h", "3h", "4h", "12h", "1d", "3d", "7d")) {
+                    double exp = tuple4.first.isEmpty() ? 0 : Double.parseDouble(tuple4.first.getOrDefault("exp_pv_" + prefix2, "0.0"));
+                    double share = tuple4.second.isEmpty() ? 0 : Double.parseDouble(tuple4.second.getOrDefault("share_pv_" + prefix2, "0.0"));
+                    double returns = tuple4.third.isEmpty() ? 0 : Double.parseDouble(tuple4.third.getOrDefault("return_uv_" + prefix2, "0.0"));
+
+                    double f1 = ExtractorUtils.calDiv(share, exp);
+                    double f2 = ExtractorUtils.calLog(share);
+                    double f3 = ExtractorUtils.calDiv(returns, exp);
+                    double f4 = ExtractorUtils.calLog(returns);
+                    double f5 = f3 * f4;
+
+                    String key1 = tuple4.name + "_" + prefix2 + "_" + "STR";
+                    String key2 = tuple4.name + "_" + prefix2 + "_" + "log(share)";
+                    String key3 = tuple4.name + "_" + prefix2 + "_" + "ROV";
+                    String key4 = tuple4.name + "_" + prefix2 + "_" + "log(return)";
+                    String key5 = tuple4.name + "_" + prefix2 + "_" + "ROV*log(return)";
+
+                    featureMap.put(key1, f1);
+                    featureMap.put(key2, f2);
+                    featureMap.put(key3, f3);
+                    featureMap.put(key4, f4);
+                    featureMap.put(key5, f5);
                 }
             }
-        }
-        final Set<String> userFeatureSet = new HashSet<>(Arrays.asList(
-                "machineinfo_brand", "machineinfo_model", "machineinfo_platform", "machineinfo_system",
-                "u_1day_exp_cnt", "u_1day_click_cnt", "u_1day_share_cnt", "u_1day_return_cnt",
-                "u_3day_exp_cnt", "u_3day_click_cnt", "u_3day_share_cnt", "u_3day_return_cnt"
-        ));
-        Iterator<Map.Entry<String, String>> iterator = userFeatureMap.entrySet().iterator();
-        while (iterator.hasNext()) {
-            Map.Entry<String, String> entry = iterator.next();
-            if (!userFeatureSet.contains(entry.getKey())) {
-                iterator.remove();
-            }
-        }
 
-        Map<String, String> f1 = RankExtractorUserFeature.getOriginFeature(userFeatureMap,
-                new HashSet<String>(Arrays.asList(
-                        "machineinfo_brand", "machineinfo_model", "machineinfo_platform", "machineinfo_system"
-                ))
-        );
-        Map<String, String> f2 = RankExtractorUserFeature.getUserRateFeature(userFeatureMap);
-        Map<String, String> f3 = RankExtractorUserFeature.cntFeatureChange(userFeatureMap,
-                new HashSet<String>(Arrays.asList(
-                        "u_1day_exp_cnt", "u_1day_click_cnt", "u_1day_share_cnt", "u_1day_return_cnt",
-                        "u_3day_exp_cnt", "u_3day_click_cnt", "u_3day_share_cnt", "u_3day_return_cnt"
-                ))
-        );
-        f1.putAll(f2);
-        f1.putAll(f3);
-
-        // 2-1: item特征处理
-        final Set<String> itemFeatureSet = new HashSet<>(Arrays.asList(
-                "total_time", "play_count_total",
-                "i_1day_exp_cnt", "i_1day_click_cnt", "i_1day_share_cnt", "i_1day_return_cnt",
-                "i_3day_exp_cnt", "i_3day_click_cnt", "i_3day_share_cnt", "i_3day_return_cnt"
-        ));
-
-        List<RankItem> rankItems = CommonCollectionUtils.toList(videos, RankItem::new);
-        List<Long> videoIds = CommonCollectionUtils.toListDistinct(videos, Video::getVideoId);
-        List<String> videoFeatureKeys = videoIds.stream().map(r -> "video_info_" + r)
-                .collect(Collectors.toList());
-        List<String> videoFeatures = redisTemplate.opsForValue().multiGet(videoFeatureKeys);
-        if (videoFeatures != null) {
-            for (int i = 0; i < videoFeatures.size(); ++i) {
-                String vF = videoFeatures.get(i);
-                Map<String, String> vfMap = new HashMap<>();
-                if (vF == null) {
-                    continue;
-                }
-                try {
-                    vfMap = JSONUtils.fromJson(vF, new TypeToken<Map<String, String>>() {
-                    }, vfMap);
-                    Map<String, String> vfMapCopy = new HashMap<>(vfMap);
-                    rankItems.get(i).setItemBasicFeature(vfMapCopy);
-                    Iterator<Map.Entry<String, String>> iteratorIn = vfMap.entrySet().iterator();
-                    while (iteratorIn.hasNext()) {
-                        Map.Entry<String, String> entry = iteratorIn.next();
-                        if (!itemFeatureSet.contains(entry.getKey())) {
-                            iteratorIn.remove();
+            Map<String, String> videoInfo = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_basic_info", new HashMap<>());
+            featureMap.put("total_time", Double.parseDouble(videoInfo.getOrDefault("total_time", "0")));
+            featureMap.put("bit_rate", Double.parseDouble(videoInfo.getOrDefault("bit_rate", "0")));
+
+            String title = videoInfo.getOrDefault("title", "");
+            if (!title.isEmpty()) {
+                for (String name : Arrays.asList("c3_feature", "c4_feature", "c5_feature", "c6_feature", "c7_feature")) {
+                    for (String key_time : Arrays.asList("tags_1d", "tags_3d", "tags_7d")) {
+                        String tags = c34567Map.getOrDefault(name + "_" + key_time, "");
+                        if (!tags.isEmpty()) {
+                            Double[] doubles = ExtractorUtils.funcC34567ForTags(tags, title);
+                            featureMap.put(name + "_" + key_time + "_matchnum", doubles[0]);
+                            featureMap.put(name + "_" + key_time + "_maxscore", doubles[1]);
+                            featureMap.put(name + "_" + key_time + "_avgscore", doubles[2]);
                         }
                     }
-                    Map<String, String> f4 = RankExtractorItemFeature.getItemRateFeature(vfMap);
-                    Map<String, String> f5 = RankExtractorItemFeature.cntFeatureChange(vfMap,
-                            new HashSet<String>(Arrays.asList(
-                                    "total_time", "play_count_total",
-                                    "i_1day_exp_cnt", "i_1day_click_cnt", "i_1day_share_cnt", "i_1day_return_cnt",
-                                    "i_3day_exp_cnt", "i_3day_click_cnt", "i_3day_share_cnt", "i_3day_return_cnt"))
-                    );
-                    f4.putAll(f5);
-                    rankItems.get(i).setFeatureMap(f4);
-                } catch (Exception e) {
-                    log.error(String.format("parse video json is wrong in {} with {}", this.CLASS_NAME, e));
                 }
             }
-        }
-        // 2-2: item 实时特征处理
-        List<String> rtFeaPartKey = new ArrayList<>(Arrays.asList("item_rt_fea_1day_partition", "item_rt_fea_1h_partition"));
-        List<String> rtFeaPartKeyResult = this.redisTemplate.opsForValue().multiGet(rtFeaPartKey);
-        Calendar calendar = Calendar.getInstance();
-        String date = new SimpleDateFormat("yyyyMMdd").format(calendar.getTime());
-        String hour = new SimpleDateFormat("HH").format(calendar.getTime());
-        String rtFeaPart1day = date + hour;
-        String rtFeaPart1h = date + hour;
-        if (rtFeaPartKeyResult != null) {
-            if (rtFeaPartKeyResult.get(0) != null) {
-                rtFeaPart1day = rtFeaPartKeyResult.get(0);
-            }
-            if (rtFeaPartKeyResult.get(1) != null) {
-                rtFeaPart1h = rtFeaPartKeyResult.get(1);
-            }
-        }
 
-        List<String> videoRtKeys1 = videoIds.stream().map(r -> "item_rt_fea_1day_" + r)
-                .collect(Collectors.toList());
-        List<String> videoRtKeys2 = videoIds.stream().map(r -> "item_rt_fea_1h_" + r)
-                .collect(Collectors.toList());
-        videoRtKeys1.addAll(videoRtKeys2);
-        List<String> videoRtFeatures = this.redisTemplate.opsForValue().multiGet(videoRtKeys1);
-
-
-        if (videoRtFeatures != null) {
-            int j = 0;
-            for (RankItem item : rankItems) {
-                String vF = videoRtFeatures.get(j);
-                ++j;
-                if (vF == null) {
-                    continue;
-                }
-                Map<String, String> vfMap = new HashMap<>();
-                Map<String, Map<String, Double>> vfMapNew = new HashMap<>();
-                try {
-                    vfMap = JSONUtils.fromJson(vF, new TypeToken<Map<String, String>>() {
-                    }, vfMap);
-                    for (Map.Entry<String, String> entry : vfMap.entrySet()) {
-                        String value = entry.getValue();
-                        if (value == null) {
-                            continue;
+            if (!vid.isEmpty()) {
+                for (String key_feature : Arrays.asList("c8_feature", "c9_feature")) {
+                    for (String key_action : Arrays.asList("share", "return")) {
+                        Map<String, String[]> cfMap = c89Map.getOrDefault(key_feature + "_" + key_action, new HashMap<>());
+                        if (cfMap.containsKey(vid)) {
+                            String[] scores = cfMap.get(vid);
+                            Double score1 = Double.parseDouble(scores[0]);
+                            Double score2 = Double.parseDouble(scores[1]);
+                            Double score3 = Double.parseDouble(scores[2]) <= 0 ? 0D : 1.0 / Double.parseDouble(scores[2]);
+                            featureMap.put(key_feature + "_" + key_action + "_score", score1);
+                            featureMap.put(key_feature + "_" + key_action + "_num", score2);
+                            featureMap.put(key_feature + "_" + key_action + "_rank", score3);
                         }
-                        String[] var1 = value.split(",");
-                        Map<String, Double> tmp = new HashMap<>();
-                        for (String var2 : var1) {
-                            String[] var3 = var2.split(":");
-                            tmp.put(var3[0], Double.valueOf(var3[1]));
-                        }
-                        vfMapNew.put(entry.getKey(), tmp);
                     }
-                } catch (Exception e) {
-                    log.error(String.format("parse video item_rt_fea_1day_ json is wrong in {} with {}", this.CLASS_NAME, e));
                 }
-                Map<String, String> f8 = RankExtractorItemFeature.getItemRealtimeRate(vfMapNew, rtFeaPart1day);
-                item.getFeatureMap().putAll(f8);
             }
-            for (RankItem item : rankItems) {
-                String vF = videoRtFeatures.get(j);
-                ++j;
-                if (vF == null) {
-                    continue;
-                }
-                Map<String, String> vfMap = new HashMap<>();
-                Map<String, Map<String, Double>> vfMapNew = new HashMap<>();
-                try {
-                    vfMap = JSONUtils.fromJson(vF, new TypeToken<Map<String, String>>() {
-                    }, vfMap);
-
-                    for (Map.Entry<String, String> entry : vfMap.entrySet()) {
-                        String value = entry.getValue();
-                        if (value == null) {
-                            continue;
-                        }
-                        String[] var1 = value.split(",");
-                        Map<String, Double> tmp = new HashMap<>();
-                        for (String var2 : var1) {
-                            String[] var3 = var2.split(":");
-                            tmp.put(var3[0], Double.valueOf(var3[1]));
-                        }
-                        vfMapNew.put(entry.getKey(), tmp);
-                    }
-                    item.setItemRealTimeFeature(vfMapNew);
-                } catch (Exception e) {
-                    log.error(String.format("parse video item_rt_fea_1h_ json is wrong in {} with {}", this.CLASS_NAME, e));
-                }
-                Map<String, String> f8 = RankExtractorItemFeature.getItemRealtimeRate(vfMapNew, rtFeaPart1h);
-                item.getFeatureMap().putAll(f8);
+            Map<String, String> d1 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_recsys_feature_cf_i2i_new", new HashMap<>());
+            if (!d1.isEmpty()) {
+                featureMap.put("d1_exp", Double.parseDouble(d1.getOrDefault("exp", "0")));
+                featureMap.put("d1_return_n", Double.parseDouble(d1.getOrDefault("return_n", "0")));
+                featureMap.put("d1_rovn", Double.parseDouble(d1.getOrDefault("rovn", "0")));
             }
+            item.featureMapDouble = featureMap;
         }
 
-
-        List<RankItem> rovRecallScore = ScorerUtils.getScorerPipeline(ScorerUtils.BASE_CONF)
-                .scoring(sceneFeatureMap, userFeatureMap, rankItems);
-        return rovRecallScore;
-    }
-
-    private Map<String, String> getSceneFeature(RankParam param) {
-        Map<String, String> sceneFeatureMap = new HashMap<>();
-        String provinceCn = param.getProvince();
-        provinceCn = provinceCn.replaceAll("省$", "");
-        sceneFeatureMap.put("ctx_region", provinceCn);
-        String city = param.getCity();
-        if ("台北市".equals(city) |
-                "高雄市".equals(city) |
-                "台中市".equals(city) |
-                "桃园市".equals(city) |
-                "新北市".equals(city) |
-                "台南市".equals(city) |
-                "基隆市".equals(city) |
-                "吉林市".equals(city) |
-                "新竹市".equals(city) |
-                "嘉义市".equals(city)
-        ) {
-        } else {
-            city = city.replaceAll("市$", "");
-        }
-        sceneFeatureMap.put("ctx_city", city);
-
-        Calendar calendar = Calendar.getInstance();
-        sceneFeatureMap.put("ctx_week", (calendar.get(Calendar.DAY_OF_WEEK) + 6) % 7 + "");
-        sceneFeatureMap.put("ctx_hour", new SimpleDateFormat("HH").format(calendar.getTime()));
-
-        return sceneFeatureMap;
-    }
-
-    @Override
-    public RankResult mergeAndSort(RankParam param, List<Video> rovVideos, List<Video> flowVideos) {
-
-        //1 兜底策略，rov池子不足时，用冷启池填补。直接返回。
-        if (CollectionUtils.isEmpty(rovVideos)) {
-            if (param.getSize() < flowVideos.size()) {
-                return new RankResult(flowVideos.subList(0, param.getSize()));
-            } else {
-                return new RankResult(flowVideos);
+        // 3 连续值特征分桶
+        readBucketFile();
+        Map<String, String> userFeatureMap = new HashMap<>(userFeatureMapDouble.size());
+        for (Map.Entry<String, Double> entry : userFeatureMapDouble.entrySet()) {
+            String name = entry.getKey();
+            Double score = entry.getValue();
+            // 注意：0值、不在分桶文件中的特征，会被过滤掉。
+            if (score > 1E-8 && this.bucketsLen.containsKey(name) && this.bucketsMap.containsKey(name)) {
+                Double bucketNum = this.bucketsLen.get(name);
+                double[] buckets = this.bucketsMap.get(name);
+                Double scoreNew = 1.0 / bucketNum * (ExtractorUtils.findInsertPosition(buckets, score) + 1.0);
+                userFeatureMap.put(name, String.valueOf(scoreNew));
             }
         }
 
-        //2 根据实验号解析阿波罗参数。
-        String abCode = param.getAbCode();
-        Map<String, Map<String, String>> rulesMap = this.filterRules.getOrDefault(abCode, new HashMap<>(0));
-
-        //3 标签读取
-        if (rulesMap != null && !rulesMap.isEmpty()) {
-            RankExtractorItemTags extractorItemTags = new RankExtractorItemTags(this.redisTemplate);
-            extractorItemTags.processor(rovVideos, flowVideos);
-        }
-        //6 合并结果时间卡控
-        if (rulesMap != null && !rulesMap.isEmpty()) {
-            RankProcessorTagFilter.processor(rovVideos, flowVideos, rulesMap);
+        for (RankItem item : rankItems) {
+            Map<String, String> featureMap = new HashMap<>();
+            Map<String, Double> featureMapDouble = item.featureMapDouble;
+
+            for (Map.Entry<String, Double> entry : featureMapDouble.entrySet()) {
+                String name = entry.getKey();
+                Double score = entry.getValue();
+                // 注意：0值、不在分桶文件中的特征，会被过滤掉。
+                if (score > 1E-8 && this.bucketsLen.containsKey(name) && this.bucketsMap.containsKey(name)) {
+                    Double bucketNum = this.bucketsLen.get(name);
+                    double[] buckets = this.bucketsMap.get(name);
+                    Double scoreNew = 1.0 / bucketNum * (ExtractorUtils.findInsertPosition(buckets, score) + 1.0);
+                    featureMap.put(name, String.valueOf(scoreNew));
+                }
+            }
+            item.featureMap = featureMap;
         }
 
-        //4 rov池提权功能
-        RankProcessorBoost.boostByTag(rovVideos, rulesMap);
-
-        //5 rov池强插功能
-        RankProcessorInsert.insertByTag(param, rovVideos, rulesMap);
+        // 排序模型
+        Map<String, String> sceneFeatureMap = new HashMap<>(0);
+        List<RankItem> items = ScorerUtils.getScorerPipeline("feeds_score_config_20240609.conf")
+                .scoring(sceneFeatureMap, userFeatureMap, rankItems);
 
-        //7 流量池按比例强插
+        // 排序融合分
+        String redisScoreKey = "redis:vid_hasreturn1d7d_rov:";
+        Map<String, Map<String, String>> vid2MapFeature = this.getVideoRedisFeature(vids, redisScoreKey);
         List<Video> result = new ArrayList<>();
-        for (int i = 0; i < param.getTopK() && i < rovVideos.size(); i++) {
-            result.add(rovVideos.get(i));
-        }
-        double flowPoolP = getFlowPoolP(param);
-        int flowPoolIndex = 0;
-        int rovPoolIndex = param.getTopK();
-        for (int i = 0; i < param.getSize() - param.getTopK(); i++) {
-            double rand = RandomUtils.nextDouble(0, 1);
-            if (rand < flowPoolP) {
-                if (flowPoolIndex < flowVideos.size()) {
-                    result.add(flowVideos.get(flowPoolIndex++));
-                } else {
-                    break;
-                }
-            } else {
-                if (rovPoolIndex < rovVideos.size()) {
-                    result.add(rovVideos.get(rovPoolIndex++));
-                } else {
-                    break;
-                }
-            }
-        }
-        if (rovPoolIndex >= rovVideos.size()) {
-            for (int i = flowPoolIndex; i < flowVideos.size() && result.size() < param.getSize(); i++) {
-                result.add(flowVideos.get(i));
+        Double chooseFunction = mergeWeight.getOrDefault("chooseFunction", 0.0);
+        Double rosDefault = mergeWeight.getOrDefault("rosDefault", 0.1);
+        Double has_1d = mergeWeight.getOrDefault("has_1d", 1.0);
+        Double has_2d = mergeWeight.getOrDefault("has_2d", 1.0);
+        Double has_3d = mergeWeight.getOrDefault("has_3d", 1.0);
+        Double has_4d = mergeWeight.getOrDefault("has_4d", 0.0);
+        Double has_5d = mergeWeight.getOrDefault("has_5d", 0.0);
+        Double has_6d = mergeWeight.getOrDefault("has_6d", 0.0);
+        Double has_7d = mergeWeight.getOrDefault("has_7d", 1.0);
+
+        for (RankItem item : items) {
+            double score = 0.0;
+            Map<String, String> hasReturnRovScoreMap = vid2MapFeature.getOrDefault(item.getVideoId() + "", new HashMap<>());
+            double hasReturnRovScore_1d = Double.parseDouble(hasReturnRovScoreMap.getOrDefault("rate_n_1d", "0"));
+            double hasReturnRovScore_2d = Double.parseDouble(hasReturnRovScoreMap.getOrDefault("rate_n_2d", "0"));
+            double hasReturnRovScore_3d = Double.parseDouble(hasReturnRovScoreMap.getOrDefault("rate_n_3d", "0"));
+            double hasReturnRovScore_4d = Double.parseDouble(hasReturnRovScoreMap.getOrDefault("rate_n_4d", "0"));
+            double hasReturnRovScore_5d = Double.parseDouble(hasReturnRovScoreMap.getOrDefault("rate_n_5d", "0"));
+            double hasReturnRovScore_6d = Double.parseDouble(hasReturnRovScoreMap.getOrDefault("rate_n_6d", "0"));
+            double hasReturnRovScore_7d = Double.parseDouble(hasReturnRovScoreMap.getOrDefault("rate_n_7d", "0"));
+            double hasReturnRovScore = (
+                    hasReturnRovScore_1d + hasReturnRovScore_2d + hasReturnRovScore_3d +
+                            hasReturnRovScore_4d + hasReturnRovScore_5d + hasReturnRovScore_6d
+                            + hasReturnRovScore_7d
+                    ) / 7;
+            if (has_1d + has_2d + has_3d + has_4d + has_5d + has_6d + has_7d > 0){
+                 hasReturnRovScore = (
+                        hasReturnRovScore_1d*has_1d + hasReturnRovScore_2d*has_2d + hasReturnRovScore_3d*has_3d +
+                                hasReturnRovScore_4d*has_4d + hasReturnRovScore_5d*has_5d + hasReturnRovScore_6d*has_6d
+                                + hasReturnRovScore_7d*has_7d
+                ) / (
+                        has_1d + has_2d + has_3d + has_4d + has_5d + has_6d + has_7d
+                );
             }
-        }
-        if (flowPoolIndex >= flowVideos.size()) {
-            for (int i = rovPoolIndex; i < rovVideos.size() && result.size() < param.getSize(); i++) {
-                result.add(rovVideos.get(i));
+            item.getScoresMap().put("hasReturnRovScore_1d", hasReturnRovScore_1d);
+            item.getScoresMap().put("hasReturnRovScore_2d", hasReturnRovScore_2d);
+            item.getScoresMap().put("hasReturnRovScore_3d", hasReturnRovScore_3d);
+            item.getScoresMap().put("hasReturnRovScore_4d", hasReturnRovScore_4d);
+            item.getScoresMap().put("hasReturnRovScore_5d", hasReturnRovScore_5d);
+            item.getScoresMap().put("hasReturnRovScore_6d", hasReturnRovScore_6d);
+            item.getScoresMap().put("hasReturnRovScore_7d", hasReturnRovScore_7d);
+            item.getScoresMap().put("hasReturnRovScore", hasReturnRovScore);
+            double fmRov = item.getScoreRov();
+            item.getScoresMap().put("fmRov", fmRov);
+            if (chooseFunction == 0){
+                score = fmRov * (rosDefault + hasReturnRovScore);
+            }else if (chooseFunction == 1){
+                score = fmRov * (1 + Math.log(hasReturnRovScore + 1));
+            }else {
+                score = fmRov * (1 + hasReturnRovScore);
             }
-        }
 
-        //8 合并结果密度控制
-        Map<String, Integer> densityRules = new HashMap<>();
-        if (rulesMap != null && !rulesMap.isEmpty()) {
-            for (Map.Entry<String, Map<String, String>> entry : rulesMap.entrySet()) {
-                String key = entry.getKey();
-                Map<String, String> value = entry.getValue();
-                if (value.containsKey("density")) {
-                    densityRules.put(key, Integer.valueOf(value.get("density")));
-                }
+            Video video = item.getVideo();
+            video.setScore(score);
+            video.setSortScore(score);
+            video.setScoresMap(item.getScoresMap());
+            video.setAllFeatureMap(item.getAllFeatureMap());
+            if (feature != null
+                    && MapUtils.isNotEmpty(feature.getVideoFeature())
+                    && MapUtils.isNotEmpty(feature.getVideoFeature().get(item.getVideoId() + ""))) {
+                video.getMetaFeatureMap().putAll(feature.getVideoFeature().get(item.getVideoId() + ""));
+            }
+            if (feature != null
+                    && MapUtils.isNotEmpty(feature.getUserFeature())) {
+                video.getMetaFeatureMap().putAll(feature.getUserFeature());
             }
+            result.add(video);
         }
-        Set<Long> videosSet = result.stream().map(Video::getVideoId).collect(Collectors.toSet());
-        List<Video> rovRecallRankNew = rovVideos.stream().filter(r -> !videosSet.contains(r.getVideoId())).collect(Collectors.toList());
-        List<Video> flowPoolRankNew = flowVideos.stream().filter(r -> !videosSet.contains(r.getVideoId())).collect(Collectors.toList());
-        List<Video> resultWithDensity = RankProcessorDensity.mergeDensityControl(result,
-                rovRecallRankNew, flowPoolRankNew, densityRules);
+        result.sort(Comparator.comparingDouble(o -> -o.getSortScore()));
 
-        return new RankResult(resultWithDensity);
+        return result;
     }
 
-    private void getVideoFeatureFromRedis(List<RankItem> items){
-        List<Long> videoIds = CommonCollectionUtils.toListDistinct(items, RankItem::getVideoId);
-        List<String> videoKeys = videoIds.stream().map(r -> "item_rt_fea_1hrootall_" + r)
-                .collect(Collectors.toList());
-        List<String> videoRtFeatures = this.redisTemplate.opsForValue().multiGet(videoKeys);
-        int j = 0;
-        if (videoRtFeatures != null) {
-            for (RankItem item : items) {
-                String vF = videoRtFeatures.get(j);
-                ++j;
-                if (vF == null) {
-                    continue;
-                }
-                Map<String, String> vfMap = new HashMap<>();
-                Map<String, Map<String, Double>> vfMapNew = new HashMap<>();
-                try {
-                    vfMap = JSONUtils.fromJson(vF, new TypeToken<Map<String, String>>() {
-                    }, vfMap);
-                    for (Map.Entry<String, String> entry : vfMap.entrySet()) {
-                        String value = entry.getValue();
-                        if (value == null) {
-                            continue;
-                        }
-                        String[] var1 = value.split(",");
-                        Map<String, Double> tmp = new HashMap<>();
-                        for (String var2 : var1) {
-                            String[] var3 = var2.split(":");
-                            tmp.put(var3[0], Double.valueOf(var3[1]));
+    private Map<String, Map<String, String>> extractVideoFeature(Map<String, Map<String, Map<String, String>>> featureMap) {
+        // TODO
+        return null;
+    }
+
+    private Map<String, String> extractSceneFeature(Map<String, Map<String, Map<String, String>>> featureMap) {
+        // TODO
+        return null;
+    }
+
+    private Map<String, String> extractUserFeature(Map<String, Map<String, Map<String, String>>> featureMap) {
+        // TODO
+        return null;
+    }
+
+    private void readBucketFile() {
+        InputStream resourceStream = RankStrategy4RegionMergeModelBasic.class.getClassLoader().getResourceAsStream("20240609_bucket_274.txt");
+        if (resourceStream != null) {
+            try (BufferedReader reader = new BufferedReader(new InputStreamReader(resourceStream))) {
+                Map<String, double[]> bucketsMap = new HashMap<>();
+                Map<String, Double> bucketsLen = new HashMap<>();
+                String line;
+                while ((line = reader.readLine()) != null) {
+                    // 替换空格和换行符，过滤空行
+                    line = line.replace(" ", "").replaceAll("\n", "");
+                    if (!line.isEmpty()) {
+                        String[] rList = line.split("\t");
+                        if (rList.length == 3) {
+                            String key = rList[0];
+                            double value1 = Double.parseDouble(rList[1]);
+                            bucketsLen.put(key, value1);
+                            double[] value2 = Arrays.stream(rList[2].split(","))
+                                    .mapToDouble(Double::valueOf)
+                                    .toArray();
+                            bucketsMap.put(key, value2);
                         }
-                        vfMapNew.put(entry.getKey(), tmp);
                     }
-                    item.setItemRealTimeRootFeature(vfMapNew);
-                } catch (Exception e) {
-                    log.error(String.format("parse video item_rt_fea_1hrootall_ json is wrong in {} with {}", this.CLASS_NAME, e));
                 }
+                this.bucketsMap = bucketsMap;
+                this.bucketsLen = bucketsLen;
+            } catch (IOException e) {
+                log.error("something is wrong in parse bucket file:" + e);
             }
+        } else {
+            log.error("no bucket file");
         }
-    }
-
-
-    public static void main(String[] args) {
-
 
     }
 
-
-
 }

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

@@ -114,7 +114,6 @@ public class RecallService implements ApplicationContextAware {
             return strategies;
         }
         switch (abCode) {
-            case "60113": // 563
             case "60105": // 551
             case "60106": // 552
             case "60107": // 553
@@ -162,6 +161,7 @@ public class RecallService implements ApplicationContextAware {
                 strategies.addAll(getRegionRecallStrategy(param));
                 strategies.add(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
                 break;
+            case "60113": // 563
             case "60114": // 564
             case "60115": // 565
             case "60119": // 569
@@ -280,12 +280,12 @@ public class RecallService implements ApplicationContextAware {
             case "60106": // 552
             case "60107": // 553
             case "60116": // 566
-            case "60113": // 563
             case "60117": // 567
             case "60656": // 656
             case "60104": // 去掉sim的对比实验
                 strategies.add(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
                 break;
+            case "60113": // 563
             case "60114": // 564
             case "60115": // 565
             case "60118": // 568