566 流量池使用自己数据做汤姆森

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

@@ -44,6 +44,8 @@ public class RankRouter {
     @Autowired
     private RankStrategy4RegionMergeModelV565 rankStrategy4RegionMergeModelV565;
     @Autowired
+    private RankStrategy4RegionMergeModelV566 rankStrategy4RegionMergeModelV566;
+    @Autowired
     private FestivalStrategy4RankModel festivalStrategy4RankModel;
 
     @Autowired
@@ -68,9 +70,10 @@ public class RankRouter {
                 return rankStrategy4RegionMergeModelV563.rank(param);
             case "60114": // 564
                 return rankStrategy4RegionMergeModelV564.rank(param);
-            case "60115":
+            case "60115": // 565
                 return rankStrategy4RegionMergeModelV565.rank(param);
-            case "60116":
+            case "60116": // 566
+                return rankStrategy4RegionMergeModelV566.rank(param);
             case "60117":
             case "60118":
             case "60120": // 576

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV547.java

@@ -189,7 +189,7 @@ public class RankStrategy4RegionMergeModelV547 extends RankService {
         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", 0.8);
+        double f = mergeWeight.getOrDefault("f", 0.6);
         double g = mergeWeight.getOrDefault("g", 2.0);
         double h = mergeWeight.getOrDefault("h", 240.0);
         double ifAdd = mergeWeight.getOrDefault("ifAdd", 1.0);

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV566.java

@@ -0,0 +1,653 @@
+package com.tzld.piaoquan.recommend.server.service.rank.strategy;
+
+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.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.springframework.stereotype.Service;
+
+import java.text.SimpleDateFormat;
+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 RankStrategy4RegionMergeModelV566 extends RankService {
+    @ApolloJsonValue("${rank.score.merge.weightv566:}")
+    private Map<String, Double> mergeWeight;
+    @ApolloJsonValue("${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());
+            }
+        }
+    }
+
+    @Override
+    public List<Video> mergeAndRankRovRecall(RankParam param) {
+        Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : new HashMap<>(0);
+        //-------------------融-------------------
+        //-------------------合-------------------
+        //-------------------逻-------------------
+        //-------------------辑-------------------
+
+        List<Video> oldRovs = new ArrayList<>();
+        oldRovs.addAll(extractAndSort(param, RegionHRecallStrategy.PUSH_FORM));
+        oldRovs.addAll(extractAndSort(param, RegionHDupRecallStrategy.PUSH_FORM));
+        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);
+        oldRovs = oldRovs.size() <= sizeReturn
+                ? oldRovs
+                : oldRovs.subList(0, sizeReturn);
+        Set<Long> setVideo = new HashSet<>();
+        this.duplicate(setVideo, oldRovs);
+
+        //-------------------地域相关召回 融合+去重-------------------
+        List<Video> rovRecallRank = new ArrayList<>();
+        List<Video> v1 = extractAndSort(param, RegionRealtimeRecallStrategyV1.PUSH_FORM);
+        List<Video> v2 = extractAndSort(param, RegionRealtimeRecallStrategyV2.PUSH_FORM);
+        List<Video> v3 = extractAndSort(param, RegionRealtimeRecallStrategyV3.PUSH_FORM);
+        List<Video> v4 = extractAndSort(param, RegionRealtimeRecallStrategyV4.PUSH_FORM);
+        this.duplicate(setVideo, v1);
+        this.duplicate(setVideo, v2);
+        this.duplicate(setVideo, v3);
+        this.duplicate(setVideo, v4);
+        //-------------------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);
+        this.duplicate(setVideo, v5);
+        this.duplicate(setVideo, v6);
+        //-------------------节日扶持召回 融合+去重-------------------
+        List<Video> v7 = extractAndSort(param, FestivalRecallStrategyV1.PUSH_FORM);
+        this.duplicate(setVideo, v7);
+
+        rovRecallRank.addAll(oldRovs);
+        rovRecallRank.addAll(v1.subList(0, Math.min(mergeWeight.getOrDefault("v1", 20.0).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", 0.0).intValue(), v4.size())));
+        rovRecallRank.addAll(v5.subList(0, Math.min(mergeWeight.getOrDefault("v5", 10.0).intValue(), v5.size())));
+        rovRecallRank.addAll(v6.subList(0, Math.min(mergeWeight.getOrDefault("v6", 10.0).intValue(), v6.size())));
+        rovRecallRank.addAll(v7.subList(0, Math.min(mergeWeight.getOrDefault("v7", 10.0).intValue(), v7.size())));
+
+        //-------------------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);
+        }
+        for (RankItem item : items) {
+            Map<String, String> itemBasicMap = item.getItemBasicFeature();
+            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
+            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", 0.6);
+        double g = mergeWeight.getOrDefault("g", 2.0);
+        double h = mergeWeight.getOrDefault("h", 240.0);
+        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 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 * share2allreturnScore + g * view2allreturnScore);
+            }
+            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)
+                );
+            }
+        }
+        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;
+        }
+
+        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));
+                }
+            }
+        }
+        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> 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;
+                        }
+                        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);
+            }
+        }
+
+
+        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);
+            }
+        }
+
+        //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);
+        }
+
+        //4 rov池提权功能
+        RankProcessorBoost.boostByTag(rovVideos, rulesMap);
+
+        //5 rov池强插功能
+        RankProcessorInsert.insertByTag(param, rovVideos, rulesMap);
+
+        //7 流量池按比例强插
+        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));
+            }
+        }
+        if (flowPoolIndex >= flowVideos.size()) {
+            for (int i = rovPoolIndex; i < rovVideos.size() && result.size() < param.getSize(); i++) {
+                result.add(rovVideos.get(i));
+            }
+        }
+
+        //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")));
+                }
+            }
+        }
+        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);
+
+        return new RankResult(resultWithDensity);
+    }
+
+    public static void main(String[] args) {
+//        String up1 = "2024031012:513,2024031013:456,2024031014:449,2024031015:262,2024031016:414,2024031017:431,2024031018:643,2024031019:732,2024031020:927,2024031021:859,2024031022:866,2024031023:358,2024031100:133,2024031101:28,2024031102:22,2024031103:15,2024031104:21,2024031105:36,2024031106:157,2024031107:371,2024031108:378,2024031109:216,2024031110:269,2024031111:299,2024031112:196,2024031113:186,2024031114:85,2024031115:82";
+        String up1 = "2024031012:1167,2024031013:1023,2024031014:947,2024031015:664,2024031016:842,2024031017:898,2024031018:1170,2024031019:1439,2024031020:2010,2024031021:1796,2024031022:1779,2024031023:722,2024031100:226,2024031101:50,2024031102:31,2024031103:30,2024031104:38,2024031105:63,2024031106:293,2024031107:839,2024031108:1250,2024031109:858,2024031110:767,2024031111:697,2024031112:506,2024031113:534,2024031114:381,2024031115:278";
+        String down1 = "2024031012:2019,2024031013:1676,2024031014:1626,2024031015:1458,2024031016:1508,2024031017:1510,2024031018:1713,2024031019:1972,2024031020:2500,2024031021:2348,2024031022:2061,2024031023:1253,2024031100:659,2024031101:243,2024031102:191,2024031103:282,2024031104:246,2024031105:439,2024031106:1079,2024031107:1911,2024031108:2023,2024031109:1432,2024031110:1632,2024031111:1183,2024031112:1024,2024031113:938,2024031114:701,2024031115:541";
+
+//        String up2 = "2024031012:215,2024031013:242,2024031014:166,2024031015:194,2024031016:209,2024031017:245,2024031018:320,2024031019:332,2024031020:400,2024031021:375,2024031022:636,2024031023:316,2024031100:167,2024031101:45,2024031102:22,2024031103:26,2024031104:12,2024031105:22,2024031106:24,2024031107:143,2024031108:181,2024031109:199,2024031110:194,2024031111:330,2024031112:423,2024031113:421,2024031114:497,2024031115:424";
+        String up2 = "2024031012:409,2024031013:464,2024031014:354,2024031015:474,2024031016:436,2024031017:636,2024031018:709,2024031019:741,2024031020:802,2024031021:904,2024031022:1112,2024031023:639,2024031100:378,2024031101:78,2024031102:47,2024031103:37,2024031104:17,2024031105:49,2024031106:103,2024031107:293,2024031108:457,2024031109:488,2024031110:558,2024031111:711,2024031112:785,2024031113:830,2024031114:974,2024031115:850";
+        String down2 = "2024031012:748,2024031013:886,2024031014:788,2024031015:1029,2024031016:957,2024031017:1170,2024031018:1208,2024031019:1181,2024031020:1275,2024031021:1265,2024031022:1512,2024031023:1190,2024031100:1127,2024031101:486,2024031102:289,2024031103:254,2024031104:197,2024031105:310,2024031106:344,2024031107:693,2024031108:976,2024031109:1045,2024031110:1039,2024031111:1257,2024031112:1202,2024031113:1454,2024031114:1785,2024031115:1544";
+
+        RankStrategy4RegionMergeModelV566 job = new RankStrategy4RegionMergeModelV566();
+        List<Double> l1 = job.getRateData(job.help(up1, "2024031115", 24), job.help(down1, "2024031115", 24), 1., 10.);
+        Double d1 = job.calScoreWeightNoTimeDecay(l1);
+
+        System.out.println(d1);
+
+        List<Double> l2 = job.getRateData(job.help(up2, "2024031115", 24), job.help(down2, "2024031115", 24), 1., 10.);
+        Double d2 = job.calScoreWeightNoTimeDecay(l2);
+
+        System.out.println(d2);
+
+    }
+
+    List<Double> help(String s, String date, Integer h) {
+        Map<String, Double> maps = Arrays.stream(s.split(",")).map(pair -> pair.split(":"))
+                .collect(Collectors.toMap(
+                        arr -> arr[0],
+                        arr -> Double.valueOf(arr[1])
+                ));
+        List<String> datehours = new LinkedList<>(); // 时间是倒叙的
+        List<Double> result = new ArrayList<>();
+        for (int i = 0; i < h; ++i) {
+            Double d = (result.isEmpty() ? 0.0 : result.get(result.size() - 1));
+            result.add(d + maps.getOrDefault(date, 0D));
+            datehours.add(date);
+            date = ExtractorUtils.subtractHours(date, 1);
+        }
+        return result;
+    }
+
+}

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

@@ -117,6 +117,7 @@ public class RecallService implements ApplicationContextAware {
                 case "60113": // 563
                 case "60114": // 564
                 case "60115": // 565
+                case "60116": // 566
                     strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV1.class.getSimpleName()));
                     strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV2.class.getSimpleName()));
                     strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV3.class.getSimpleName()));
@@ -134,7 +135,7 @@ public class RecallService implements ApplicationContextAware {
                     strategies.addAll(getRegionRecallStrategy(param));
             }
             //2:通过“流量池标记”控制“流量池召回子策略” 其中有9组会走EXPERIMENTAL_FLOW_SET_LEVEL 有1组会走EXPERIMENTAL_FLOW_SET_LEVEL_SCORE
-            if ("60111".equals(abCode)){
+            if ("60116".equals(abCode)){
                 int lastDigit = param.getLastDigit();
                 String lastDigitAB = lastDigitAbcode != null? lastDigitAbcode.getOrDefault(lastDigit, "default"): "default";
                 switch (lastDigitAB){
@@ -142,7 +143,7 @@ public class RecallService implements ApplicationContextAware {
                         strategies.add(strategyMap.get(FlowPoolWithLevelRecallStrategyFilterDigit.class.getSimpleName()));
                         break;
                     case "tomson":
-                        strategies.add(strategyMap.get(FlowPoolWithLevelRecallStrategyTomsonFilterDigit.class.getSimpleName()));
+                        strategies.add(strategyMap.get(FlowPoolWithLevelRecallStrategyTomsonFilterDigitV2.class.getSimpleName()));
                         break;
                     case "score":
                         strategies.add(strategyMap.get(FlowPoolWithLevelScoreRecallStrategy.class.getSimpleName()));
@@ -201,7 +202,6 @@ public class RecallService implements ApplicationContextAware {
             // todo 做兜底吗？
         } else {
             switch (abCode) {
-                case "60116":
                 case "60117":
                 case "60118":
                 case "60119":
@@ -240,6 +240,7 @@ public class RecallService implements ApplicationContextAware {
                 case "60113": // 563
                 case "60114": // 564
                 case "60115": // 565
+                case "60116": // 566
                     strategies.add(strategyMap.get(SimHotVideoRecallStrategy.class.getSimpleName()));
                     strategies.add(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
                     strategies.add(strategyMap.get(FestivalRecallStrategyV1.class.getSimpleName()));

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/recall/strategy/FlowPoolWithLevelRecallStrategyTomsonFilterDigitV2.java

@@ -0,0 +1,164 @@
+package com.tzld.piaoquan.recommend.server.service.recall.strategy;
+
+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 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.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_FORMAT;
+
+/**
+ * @author zhangbo
+ */
+@Service
+@Slf4j
+public class FlowPoolWithLevelRecallStrategyTomsonFilterDigitV2 extends AbstractFlowPoolWithLevelRecallStrategy {
+
+    @Autowired
+    private FlowPoolConfigService flowPoolConfigService;
+
+    @Override
+    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_FORMAT, param.getAppType(), entry.getKey());
+            if (Boolean.TRUE.equals(redisTemplate.hasKey(levelKey))) {
+                LevelWeight lw = new LevelWeight();
+                lw.setLevel(entry.getKey());
+                lw.setLevelKey(levelKey);
+                lw.setWeight(entry.getValue());
+                availableLevels.add(lw);
+            }
+        }
+        if (CollectionUtils.isEmpty(availableLevels)) {
+            return Pair.of("", "");
+        }
+
+        // 3. 根据可分发层级权重设置分发概率
+        availableLevels.sort(Comparator.comparingDouble(LevelWeight::getWeight));
+
+        double weightSum = availableLevels.stream().mapToDouble(o -> o.getWeight()).sum();
+        BigDecimal weightSumBD = new BigDecimal(weightSum);
+        double level_p_low = 0;
+        double weight_temp = 0;
+        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());
+            level_p_up = bd.divide(weightSumBD, 2, RoundingMode.HALF_UP).doubleValue();
+            LevelP levelP = new LevelP();
+            levelP.setMin(level_p_low);
+            levelP.setMax(level_p_up);
+            levelP.setLevelKey(lw.getLevelKey());
+            level_p_mapping.put(lw.level, levelP);
+            level_p_low = level_p_up;
+
+            weight_temp += lw.getWeight();
+        }
+
+        // 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()
+                    && random_p <= entry.getValue().getMax()) {
+                return Pair.of(entry.getValue().getLevelKey(), entry.getKey());
+            }
+        }
+        return Pair.of("", "");
+    }
+
+    @Data
+    static class LevelWeight {
+        private String level;
+        private String levelKey;
+        private Double weight;
+    }
+
+    @Data
+    static class LevelP {
+        private String levelKey;
+        private double min;
+        private double max;
+    }
+
+    @Override
+    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();
+        List<String> data = Objects.requireNonNull(redisTemplate.opsForSet().members(flowPoolKey)).stream().filter(o ->
+                NumberUtils.toLong(o.split("-")[0], 0) % 10 == param.getLastDigit()).distinct().collect(Collectors.toList());
+
+        if (CollectionUtils.isEmpty(data)) {
+            return null;
+        }
+        Map<String, String> videoFlowPoolMap = new LinkedHashMap<>();
+        Map<Long, String> videoFlowPoolMap_ = new LinkedHashMap<>();
+        for (String value : data) {
+            String[] values = value.split("-");
+            videoFlowPoolMap.put(values[0], values[1]);
+            videoFlowPoolMap_.put(NumberUtils.toLong(values[0], 0), values[1]);
+        }
+        ScorerPipeline4Recall pipeline = ScorerUtils.getScorerPipeline4Recall("feeds_recall_config_tomson_v2.conf");
+        List<List<Pair<Long, Double>>> results = pipeline.recall(videoFlowPoolMap);
+        List<Pair<Long, Double>> result = results.get(0);
+        Map<Long, Double> 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, result.stream()
+                .map(Pair::getLeft)
+                .collect(Collectors.toList()));
+        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())) {
+            filterResult.getVideoIds().forEach(vid -> {
+                Video recallData = new Video();
+                recallData.setVideoId(vid);
+                recallData.setAbCode(param.getAbCode());
+                recallData.setRovScore(resultmap.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;
+    }
+}

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

@@ -43,6 +43,7 @@ public final class ScorerUtils {
         ScorerUtils.init4Recall("feeds_score_config_festival.conf");
         ScorerUtils.init4Recall("feeds_score_config_bless.conf");
         ScorerUtils.init4Recall("feeds_recall_config_tomson.conf");
+        ScorerUtils.init4Recall("feeds_recall_config_tomson_v2.conf");
     }
 
     private ScorerUtils() {

recommend-server-service/src/main/resources/feeds_recall_config_tomson_v2.conf

@@ -0,0 +1,8 @@
+scorer-config = {
+    score1-config = {
+        scorer-name = "com.tzld.piaoquan.recommend.server.service.score4recall.strategy.FlowPoolScorer"
+        scorer-priority = 99
+        model-path = "alg_recall_file/10_coldstart_tomson_v2.txt"
+    }
+
+}