ADD: CB Recall and rank

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

@@ -52,6 +52,8 @@ public class RankRouter {
     @Autowired
     private RankStrategy4RegionMergeModelV569 rankStrategy4RegionMergeModelV569;
     @Autowired
+    private RankStrategy4RegionMergeModelV650 rankStrategy4RegionMergeModelV650;
+    @Autowired
     private FestivalStrategy4RankModel festivalStrategy4RankModel;
 
     @Autowired
@@ -112,8 +114,10 @@ public class RankRouter {
             case "60131":
             case "60132":
                 return festivalStrategy4RankModel.rank(param);
-            case "60150":
+            case "60150": // 645
                 return rankStrategy4ShareDeepAndWidth.rank(param);
+            case "60151": // 650
+                return rankStrategy4RegionMergeModelV650.rank(param);
             default:
                 break;
         }

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV650.java

@@ -0,0 +1,386 @@
+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.RankExtractorItemTags;
+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.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.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 RankStrategy4RegionMergeModelV650 extends RankService {
+    @ApolloJsonValue("${rank.score.merge.weightv650:}")
+    private Map<String, Double> mergeWeight;
+    @ApolloJsonValue("${RankStrategy4DensityFilterV2:}")
+    private final 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));
+        removeDuplicate(oldRovs);
+        int sizeReturn = param.getSize();
+        //-------------------老地域召回-------------------
+        List<Video> v0 = oldRovs.size() <= sizeReturn
+                ? oldRovs
+                : oldRovs.subList(0, sizeReturn);
+        List<Video> v5 = extractAndSort(param, SimHotVideoRecallStrategy.PUSH_FORM);
+        List<Video> v6 = extractAndSort(param, ReturnVideoRecallStrategy.PUSH_FORM);
+        //-------------------新地域召回------------------
+        List<Video> v1 = extractAndSort(param, RegionRealtimeRecallStrategyV1.PUSH_FORM);
+        //-------------------节日特殊召回-------------------
+        List<Video> v7 = extractAndSort(param, FestivalRecallStrategyV1.PUSH_FORM);
+        //-------------------基于CB的tag召回-------------------
+        List<Video> v10 = extractAndSort(param, ContentBaseRecallStrategy.PUSH_FORM);
+
+
+        Set<Long> setVideo = new HashSet<>();
+        this.duplicate(setVideo, v0);
+        this.duplicate(setVideo, v5);
+        this.duplicate(setVideo, v6);
+        this.duplicate(setVideo, v1);
+        this.duplicate(setVideo, v7);
+        this.duplicate(setVideo, v10);
+
+        List<Video> rovRecallRank = new ArrayList<>();
+        rovRecallRank.addAll(v0);
+        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(v1.subList(0, Math.min(mergeWeight.getOrDefault("v1", 5.0).intValue(), v1.size())));
+        rovRecallRank.addAll(v7.subList(0, Math.min(mergeWeight.getOrDefault("v7", 5.0).intValue(), v7.size())));
+        rovRecallRank.addAll(v10.subList(0, Math.min(mergeWeight.getOrDefault("v10", 6.0).intValue(), v10.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, Map<String, Double>> itemRealMap = item.getItemRealTimeFeature();
+            List<Double> views = getStaticData(itemRealMap, datehours, "view_uv_list_1h");
+            List<Double> shares = getStaticData(itemRealMap, datehours, "share_uv_list_1h");
+            List<Double> allreturns = getStaticData(itemRealMap, datehours, "return_uv_list_1h");
+
+            // 全部回流的rov和ros
+            List<Double> share2allreturn = getRateData(allreturns, shares, 0.0, 0.0);
+            Double share2allreturnScore = calScoreWeightNoTimeDecay(share2allreturn);
+            item.scoresMap.put("share2allreturnScore", share2allreturnScore);
+            List<Double> view2allreturn = getRateData(allreturns, views, 0.0, 0.0);
+            Double view2allreturnScore = calScoreWeightNoTimeDecay(view2allreturn);
+            item.scoresMap.put("view2allreturnScore", view2allreturnScore);
+
+            // 全部回流
+            Double allreturnsScore = calScoreWeightNoTimeDecay(allreturns);
+            item.scoresMap.put("allreturnsScore", allreturnsScore);
+
+
+        }
+        // 3 融合公式
+        List<Video> result = new ArrayList<>();
+        double f = mergeWeight.getOrDefault("f", 0.1);
+        double g = mergeWeight.getOrDefault("g", 1.0);
+        for (RankItem item : items) {
+            double share2allreturnScore = item.scoresMap.getOrDefault("share2allreturnScore", 0.0);
+            double view2allreturnScore = item.scoresMap.getOrDefault("view2allreturnScore", 0.0);
+            double score = 0.0;
+            double allreturnsScore = item.scoresMap.getOrDefault("allreturnsScore", 0.0);
+            if (allreturnsScore > 50) {
+                score += (f * share2allreturnScore + g * view2allreturnScore);
+            } else {
+                score += (f * share2allreturnScore + g * view2allreturnScore) * 0.01;
+            }
+            Video video = item.getVideo();
+            video.setScore(score);
+            video.setSortScore(score);
+            video.setScoreStr(item.getScoreStr());
+            video.setScoresMap(item.getScoresMap());
+            result.add(video);
+        }
+        result.sort(Comparator.comparingDouble(o -> -o.getSortScore()));
+        return result;
+    }
+
+    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;
+        }
+
+
+        List<RankItem> rankItems = CommonCollectionUtils.toList(videos, RankItem::new);
+        List<Long> videoIds = CommonCollectionUtils.toListDistinct(videos, Video::getVideoId);
+
+        // 2-2: item 实时特征处理
+        List<String> videoRtKeys2 = videoIds.stream().map(r -> "item_rt_fea_1h_" + r)
+                .collect(Collectors.toList());
+        List<String> videoRtFeatures = this.redisTemplate.opsForValue().multiGet(videoRtKeys2);
+
+
+        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);
+                    }
+                    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));
+                }
+            }
+        }
+
+        return rankItems;
+    }
+
+    @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";
+
+        RankStrategy4RegionMergeModelV569 job = new RankStrategy4RegionMergeModelV569();
+        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/rank/strategy/RankStrategy4ShareDeepAndWidth.java

@@ -8,9 +8,7 @@ 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;
@@ -21,9 +19,6 @@ 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.beans.factory.annotation.Autowired;
-import org.springframework.beans.factory.annotation.Qualifier;
-import org.springframework.data.redis.core.RedisTemplate;
 import org.springframework.stereotype.Service;
 
 import java.text.SimpleDateFormat;
@@ -37,19 +32,12 @@ import java.util.stream.Collectors;
 @Service
 @Slf4j
 public class RankStrategy4ShareDeepAndWidth extends RankService {
-    @ApolloJsonValue("${rank.score.merge.weight:ShareDeepAndWidth:}")
+    @ApolloJsonValue("${rank.score.merge.weightv645:}")
     private Map<String, Double> mergeWeight;
     @ApolloJsonValue("${RankStrategy4DensityFilterV2:}")
     private final Map<String, Map<String, Map<String, String>>> filterRules = new HashMap<>();
     final private String CLASS_NAME = this.getClass().getSimpleName();
 
-    @Autowired
-    @Qualifier("featureRedisTemplate")
-    private RedisTemplate<String, String> featureRedisTemplate;
-
-    public RankStrategy4ShareDeepAndWidth() {
-    }
-
     public void duplicate(Set<Long> setVideo, List<Video> videos) {
         Iterator<Video> iterator = videos.iterator();
         while (iterator.hasNext()) {
@@ -65,27 +53,49 @@ public class RankStrategy4ShareDeepAndWidth extends RankService {
     @Override
     public List<Video> mergeAndRankRovRecall(RankParam param) {
         Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : new HashMap<>(0);
-        //-------------------相关性召回 融合+去重-------------------
+        //-------------------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));
+        removeDuplicate(oldRovs);
+        int sizeReturn = param.getSize();
+        List<Video> v0 = oldRovs.size() <= sizeReturn
+                ? oldRovs
+                : oldRovs.subList(0, sizeReturn);
         Set<Long> setVideo = new HashSet<>();
-        List<Video> v1 = extractAndSort(param, SimHotVideoRecallStrategy.PUSH_FORM);
-        List<Video> v2 = extractAndSort(param, ReturnVideoRecallStrategy.PUSH_FORM);
-        this.duplicate(setVideo, v1);
-        this.duplicate(setVideo, v2);
-        //-------------------流量池直接送 融合+去重-------------------
-        List<Video> v3 = extractAndSort(param, FlowPoolLastDayTopRecallStrategy.PUSH_FORM);
-        this.duplicate(setVideo, v3);
-        //-------------------分享深度召回 融合+去重-------------------
-        List<Video> v4 = extractAndSort(param, ShareDeepRecallStrategy.PUSH_FORM);
-        this.duplicate(setVideo, v4);
-        //-------------------分享广度召回 融合+去重-------------------
-        List<Video> v5 = extractAndSort(param, ShareWidthRecallStrategy.PUSH_FORM);
+        this.duplicate(setVideo, v0);
+
+
+        //-------------------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);
+        //-------------------分享深度、广度 融合+去重-------------------
+        List<Video> v9 = extractAndSort(param, ShareWidthRecallStrategy.PUSH_FORM);
+        List<Video> v10 = extractAndSort(param, ShareDeepRecallStrategy.PUSH_FORM);
+        this.duplicate(setVideo, v9);
+        //-------------------地域相关召回 融合+去重-------------------
+        List<Video> v1 = extractAndSort(param, RegionRealtimeRecallStrategyV1.PUSH_FORM);
+        this.duplicate(setVideo, v1);
+        //-------------------节日扶持召回 融合+去重-------------------
+        List<Video> v7 = extractAndSort(param, FestivalRecallStrategyV1.PUSH_FORM);
+        this.duplicate(setVideo, v7);
         List<Video> rovRecallRank = new ArrayList<>();
-        rovRecallRank.addAll(v1.subList(0, Math.min(mergeWeight.getOrDefault("v1", 5.0).intValue(), v1.size())));
-        rovRecallRank.addAll(v2.subList(0, Math.min(mergeWeight.getOrDefault("v2", 5.0).intValue(), v2.size())));
-        rovRecallRank.addAll(v3.subList(0, Math.min(mergeWeight.getOrDefault("v3", 5.0).intValue(), v3.size())));
-        rovRecallRank.addAll(v4.subList(0, Math.min(mergeWeight.getOrDefault("v4", 5.0).intValue(), v4.size())));
+        rovRecallRank.addAll(v0);
         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(v9.subList(0, Math.min(mergeWeight.getOrDefault("v9", 5.0).intValue(), v9.size())));
+        rovRecallRank.addAll(v10.subList(0, Math.min(mergeWeight.getOrDefault("v10", 5.0).intValue(), v10.size())));
+        rovRecallRank.addAll(v1.subList(0, Math.min(mergeWeight.getOrDefault("v1", 5.0).intValue(), v1.size())));
+        rovRecallRank.addAll(v7.subList(0, Math.min(mergeWeight.getOrDefault("v7", 5.0).intValue(), v7.size())));
 
         //-------------------排-------------------
         //-------------------序-------------------
@@ -148,14 +158,20 @@ public class RankStrategy4ShareDeepAndWidth extends RankService {
             double share2allreturnScore = item.scoresMap.getOrDefault("share2allreturnScore", 0.0);
             double view2allreturnScore = item.scoresMap.getOrDefault("view2allreturnScore", 0.0);
             double score = 0.0;
-            double allShareCount = item.scoresMap.getOrDefault("sumShareCount", 0.0);
-            double daySharedepthMaxAvg = Double.parseDouble(featureMap.getOrDefault("i_1day_sharedepth_max_avg", "0.0"));
-            double daySharewidthMaxAvg = Double.parseDouble(featureMap.getOrDefault("i_1day_sharewidth_max_avg", "0.0"));
-            if (allShareCount > 50) {
-                score += a * daySharedepthMaxAvg + b * daySharewidthMaxAvg * 0.05;
+            double allreturnsScore = item.scoresMap.getOrDefault("allreturnsScore", 0.0);
+            if (allreturnsScore > 50) {
+                score += (f * share2allreturnScore + g * view2allreturnScore);
             } else {
                 score += (f * share2allreturnScore + g * view2allreturnScore) * 0.01;
             }
+            double sumShareCount = item.scoresMap.getOrDefault("sumShareCount", 0.0);
+            double daySharedepthMaxAvg = Double.parseDouble(featureMap.getOrDefault("i_1day_sharedepth_max_avg", "0.0"));
+            double daySharewidthMaxAvg = Double.parseDouble(featureMap.getOrDefault("i_1day_sharewidth_max_avg", "0.0"));
+
+            if (sumShareCount > 30) {
+                score += a * daySharedepthMaxAvg + b * daySharewidthMaxAvg;
+            }
+
             Video video = item.getVideo();
             video.setScore(score);
             video.setSortScore(score);
@@ -210,98 +226,9 @@ public class RankStrategy4ShareDeepAndWidth extends RankService {
             return result;
         }
 
-        // 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 = featureRedisTemplate.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 = featureRedisTemplate.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);
@@ -309,14 +236,10 @@ public class RankStrategy4ShareDeepAndWidth extends RankService {
         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)
@@ -356,9 +279,6 @@ public class RankStrategy4ShareDeepAndWidth extends RankService {
                 } 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);
-
 
                 String k1 = "sharedepth_max_avg_list_1day";
                 String k2 = "sharewidth_max_avg_list_1day";
@@ -401,44 +321,12 @@ public class RankStrategy4ShareDeepAndWidth extends RankService {
                 } 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);
             }
         }
 
         return rankItems;
     }
 
-    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) {
 
@@ -535,7 +423,7 @@ public class RankStrategy4ShareDeepAndWidth extends RankService {
         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";
 
-        RankStrategy4ShareDeepAndWidth job = new RankStrategy4ShareDeepAndWidth();
+        RankStrategy4RegionMergeModelV567 job = new RankStrategy4RegionMergeModelV567();
         List<Double> l1 = job.getRateData(job.help(up1, "2024031115", 24), job.help(down1, "2024031115", 24), 1., 10.);
         Double d1 = job.calScoreWeightNoTimeDecay(l1);
 

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

@@ -145,12 +145,18 @@ public class RecallService implements ApplicationContextAware {
                     strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV3.class.getSimpleName()));
                     strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV4.class.getSimpleName()));
                     break;
-                case "60150":
-                    strategies.add(strategyMap.get(SimHotVideoRecallStrategy.class.getSimpleName()));
-                    strategies.add(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
+                case "60150": // 645
+                    strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV1.class.getSimpleName()));
                     strategies.add(strategyMap.get(ShareWidthRecallStrategy.class.getSimpleName()));
                     strategies.add(strategyMap.get(ShareDeepRecallStrategy.class.getSimpleName()));
                     break;
+                case "60151": // 650
+                    strategies.addAll(getRegionRecallStrategy(param));
+                    strategies.add(strategyMap.get(SimHotVideoRecallStrategy.class.getSimpleName()));
+                    strategies.add(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
+                    strategies.add(strategyMap.get(ContentBaseRecallStrategy.class.getSimpleName()));
+                    strategies.add(strategyMap.get(FestivalRecallStrategyV1.class.getSimpleName()));
+                    break;
                 case "60117": // 567
                 case "60118": // 568
                     strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV1.class.getSimpleName()));
@@ -277,6 +283,7 @@ public class RecallService implements ApplicationContextAware {
                 case "60115": // 565
                 case "60117": // 567
                 case "60118": // 568
+                case "60150": // 645
                     if (!hitUserBlacklist) {
                         strategies.add(strategyMap.get(FlowPoolLastDayTopRecallStrategy.class.getSimpleName()));
                     }

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

@@ -1,5 +1,6 @@
 package com.tzld.piaoquan.recommend.server.service.recall.strategy;
 
+import com.alibaba.fastjson.JSONObject;
 import com.google.common.collect.Lists;
 import com.tzld.piaoquan.recommend.server.model.Video;
 import com.tzld.piaoquan.recommend.server.service.filter.FilterParam;
@@ -12,6 +13,8 @@ import com.tzld.piaoquan.recommend.server.service.score.ScorerUtils;
 import com.tzld.piaoquan.recommend.server.service.score4recall.ScorerPipeline4Recall;
 import org.apache.commons.collections4.CollectionUtils;
 import org.apache.commons.lang3.tuple.Pair;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.data.redis.core.RedisTemplate;
 import org.springframework.stereotype.Component;
 
 import javax.annotation.Resource;
@@ -25,21 +28,27 @@ public class ContentBaseRecallStrategy implements RecallStrategy {
 
     public static final String PUSH_FORM = "content_base_recall_strategy";
 
+    private static final String VIDEO_2_TAG_REDIS_KEY = "content:base:video:tags:";
+
+    @Value("${recommend.content.base.recall.strategy.limit:50}")
+    private Integer limit;
     @Resource
     private RegionFilterService filterService;
+    @Resource
+    private RedisTemplate<String, String> redisTemplate;
 
     @Override
     public List<Video> recall(RecallParam param) {
-
-        // 1 获取省份key 放入参数map中
-        String provinceCn = param.getProvince();
-        if (provinceCn == null) {
-            provinceCn = "中国";
-        } else {
-            provinceCn = provinceCn.replaceAll("省$", "");
-        }
+        Long videoId = param.getVideoId();
         Map<String, String> param4Model = new HashMap<>(1);
-        param4Model.put("region_province", provinceCn);
+        String redisKey = VIDEO_2_TAG_REDIS_KEY + videoId;
+        String tags = redisTemplate.opsForValue().get(redisKey);
+        try {
+            String tagStr = JSONObject.parseObject(tags).getString("tags");
+            param4Model.put("tags", tagStr);
+        } catch (Exception e) {
+            return Collections.emptyList();
+        }
         // 2 通过model拿到召回list
         ScorerPipeline4Recall pipeline = ScorerUtils.getScorerPipeline4Recall("content_base_recall.conf");
         List<List<Pair<Long, Double>>> results = pipeline.recall(param4Model);
@@ -58,7 +67,7 @@ public class ContentBaseRecallStrategy implements RecallStrategy {
         FilterResult filterResult = filterService.filter(filterParam);
         List<Video> videosResult = new ArrayList<>();
         if (filterResult != null && CollectionUtils.isNotEmpty(filterResult.getVideoIds())) {
-            filterResult.getVideoIds().forEach(vid -> {
+            filterResult.getVideoIds().stream().limit(limit).forEach(vid -> {
                 Video video = new Video();
                 video.setVideoId(vid);
                 video.setAbCode(param.getAbCode());

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

@@ -46,6 +46,7 @@ public final class ScorerUtils {
         ScorerUtils.init4Recall("feeds_recall_config_tomson_v2.conf");
         ScorerUtils.init4Recall("feeds_score_config_share_width.conf");
         ScorerUtils.init4Recall("feeds_score_config_share_deep.conf");
+        ScorerUtils.init4Recall("content_base_recall.conf");
     }
 
     private ScorerUtils() {

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/score4recall/model4recall/VideoTagModel4RecallMap.java

@@ -0,0 +1,44 @@
+package com.tzld.piaoquan.recommend.server.service.score4recall.model4recall;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStreamReader;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+
+public class VideoTagModel4RecallMap extends AbstractModel {
+    private static final Logger LOGGER = LoggerFactory.getLogger(VideoTagModel4RecallMap.class);
+    public Map<String, List<Long>> map = new HashMap<>();
+
+    @Override
+    public boolean loadFromStream(InputStreamReader in) throws IOException {
+        BufferedReader input = new BufferedReader(in);
+        String line;
+        while ((line = input.readLine()) != null) {
+            String[] items = line.split("\t");
+            if (items.length < 2) {
+                continue;
+            }
+            String videoIds = items[1].trim();
+            try {
+                String[] videoIdArr = videoIds.split(",");
+                List<Long> videoIdList = new ArrayList<>();
+                for (String videoId : videoIdArr) {
+                    videoIdList.add(Long.parseLong(videoId));
+                }
+                map.put(items[0], videoIdList);
+            } catch (Exception e) {
+                LOGGER.error(String.format("VideoTagModel4RecallMap is wrong with parse %s: ", line), e);
+            }
+        }
+        input.close();
+        in.close();
+        return true;
+    }
+}

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/score4recall/strategy/ContentBaseRecallScore.java

@@ -2,12 +2,16 @@ package com.tzld.piaoquan.recommend.server.service.score4recall.strategy;
 
 import com.tzld.piaoquan.recommend.server.service.score.ScorerConfigInfo;
 import com.tzld.piaoquan.recommend.server.service.score4recall.AbstractScorer4Recall;
-import com.tzld.piaoquan.recommend.server.service.score4recall.model4recall.Model4RecallKeyValue;
+import com.tzld.piaoquan.recommend.server.service.score4recall.model4recall.VideoTagModel4RecallMap;
+import org.apache.commons.collections4.CollectionUtils;
+import org.apache.commons.lang3.StringUtils;
 import org.apache.commons.lang3.tuple.Pair;
 
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 import java.util.Map;
+import java.util.stream.Collectors;
 
 
 public class ContentBaseRecallScore extends AbstractScorer4Recall {
@@ -18,17 +22,30 @@ public class ContentBaseRecallScore extends AbstractScorer4Recall {
 
     @Override
     public void loadModel() {
-        doLoadModel(Model4RecallKeyValue.class);
+        doLoadModel(VideoTagModel4RecallMap.class);
     }
 
     @Override
     public List<Pair<Long, Double>> recall(Map<String, String> params) {
-        Model4RecallKeyValue model = (Model4RecallKeyValue) this.getModel();
-        if (model == null || model.kv == null) {
-            return new ArrayList<>();
+        VideoTagModel4RecallMap model = (VideoTagModel4RecallMap) this.getModel();
+        List<Pair<Long, Double>> result = new ArrayList<>();
+        if (model == null || model.map == null) {
+            return result;
         }
-        List<Pair<Long, Double>> festivalLists = model.kv.getOrDefault("", new ArrayList<>());
-        return null;
+        String tags = params.get("tags");
+        if (StringUtils.isBlank(tags)) {
+            return result;
+        }
+        List<String> tagList = Arrays.stream(tags.split(",")).collect(Collectors.toList());
+        Map<String, List<Long>> tagVideoIdMap = model.map;
+        for (String tag : tagList) {
+            List<Long> videoIds = tagVideoIdMap.get(tag);
+            if (CollectionUtils.isNotEmpty(videoIds)) {
+                result.addAll(videoIds.stream().map(videoId -> Pair.of(videoId, 1.0))
+                        .collect(Collectors.toList()));
+            }
+        }
+        return result;
     }