Merge remote-tracking branch 'origin/feature/zhangbo_rank' into feature/sunxiaoyi_recall

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

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/common/enums/AppTypeEnum.java

@@ -46,6 +46,7 @@ public enum AppTypeEnum {
     ZUI_JING_QI(19, "票圈最惊奇"),
     PIAO_QUAN_VIDEO_PLUS(21, "票圈视频+"),
     JOURNEY(22, "票圈足迹"),
+    PIAO_QUAN_MEIHAO_ZHUFU(25, "票圈美好祝福"),
     ADMIN_CRAWLER(888888, "后台-爬虫"),
     OTHERS(999999, "其他的调用方，比如后台管理系统");
 

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/filter/AbstractFilterService.java

@@ -102,7 +102,7 @@ public abstract class AbstractFilterService {
             futures.add(future);
         }
         try {
-            cdl.await(200, TimeUnit.MILLISECONDS);
+            cdl.await(150, TimeUnit.MILLISECONDS);
         } catch (InterruptedException e) {
             log.error("filter error", e);
             return null;

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

@@ -27,6 +27,8 @@ public class RankRouter {
     @Autowired
     private RankStrategy4RegionMergeModelV5 rankStrategy4RegionMergeModelV5;
     @Autowired
+    private RankStrategy4RegionMergeModelV6 rankStrategy4RegionMergeModelV6;
+    @Autowired
     private FestivalStrategy4RankModel festivalStrategy4RankModel;
 
     @Autowired
@@ -70,6 +72,8 @@ public class RankRouter {
                 return rankStrategy4RegionMergeModelV4.rank(param);
             case "60125": // 547
                 return rankStrategy4RegionMergeModelV5.rank(param);
+            case "60126": // 548
+                return rankStrategy4RegionMergeModelV6.rank(param);
             case "60130":
                 // 先走默认排序，后续需要优化祝福类的视频排序
                 return festivalStrategy4RankModel.rank(param);

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

@@ -65,7 +65,28 @@ public class RankService {
             }
             return null;
         }
+        // 1 通过 apptype 判断该小程序走怎样的排序策略。
+        if (param.getAppType() == AppTypeEnum.PIAO_QUAN_MEIHAO_ZHUFU.getCode()){
+            List<Video> results = new ArrayList<>();
+            results.addAll(extractAndSort(param, FestivalRecallStrategyV1.PUSH_FORM));
+            List<String> videoIdKeys = results.stream()
+                    .map(t -> param.getRankKeyPrefix() + t.getVideoId())
+                    .collect(Collectors.toList());
+            List<String> videoScores = redisTemplate.opsForValue().multiGet(videoIdKeys);
+            if (CollectionUtils.isNotEmpty(videoScores)
+                    && videoScores.size() == results.size()) {
+                for (int i = 0; i < videoScores.size(); i++) {
+                    results.get(i).setSortScore(NumberUtils.toDouble(videoScores.get(i), 0.0));
+                }
+                Collections.sort(results, Comparator.comparingDouble(o -> -o.getSortScore()));
+            }
+            results.addAll(extractAndSort(param, RegionRealtimeRecallStrategyV2.PUSH_FORM));
+            results.addAll(extractAndSort(param, RegionRealtimeRecallStrategyV3.PUSH_FORM));
+            removeDuplicate(results);
+            return new RankResult(results);
+        }
 
+        // 2 正常走分发 排序+冷启动
         List<Video> rovRecallRank = mergeAndRankRovRecall(param);
         // log.info("mergeAndRankRovRecall rovRecallRank={}", JSONUtils.toJson(rovRecallRank));
         List<Video> flowPoolRank = mergeAndRankFlowPoolRecall(param);
@@ -90,14 +111,7 @@ public class RankService {
     public List<Video> mergeAndRankRovRecall(RankParam param) {
         // TODO ab test
         // TODO 抽象成Strategy
-//        boolean hitTest = newRankSwitch
-//                || CommonCollectionUtils.contains(param.getAbExpCodes(), newRankAbExpCode);
-//        if (hitTest) {
-//            return mergeAndRankRovRecallNew(param);
-//        } else {
-//            return mergeAndRankRovRecallOld(param);
-//        }
-        // 研发重写了代码，直接走新逻辑
+
         return mergeAndRankRovRecallOld(param);
     }
 

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

@@ -0,0 +1,51 @@
+package com.tzld.piaoquan.recommend.server.service.rank.extractor;
+import com.tzld.piaoquan.recommend.server.model.Video;
+import org.springframework.data.redis.core.RedisTemplate;
+
+import java.util.*;
+
+public class RankExtractorItemTags {
+    public RedisTemplate<String, String> redisTemplate;
+    public RankExtractorItemTags(RedisTemplate<String, String> redisTemplate){
+        this.redisTemplate = redisTemplate;
+    }
+
+    public void processor(List<Video> rovVideos, List<Video> flowVideos){
+        List<Long> videoIds = new ArrayList<>();
+        for (Video v : rovVideos) {
+            videoIds.add(v.getVideoId());
+        }
+        for (Video v : flowVideos) {
+            videoIds.add(v.getVideoId());
+        }
+        Map<Long, List<String>> videoTagDict = getVideoTags(redisTemplate, videoIds);
+        for (Video v : rovVideos) {
+            v.setTags(videoTagDict.getOrDefault(v.getVideoId(), new ArrayList<>()));
+        }
+        for (Video v : flowVideos) {
+            v.setTags(videoTagDict.getOrDefault(v.getVideoId(), new ArrayList<>()));
+        }
+    }
+
+    public static Map<Long, List<String>> getVideoTags(RedisTemplate<String, String> redisHelper, List<Long> videoIds) {
+        String REDIS_PREFIX = "alg_recsys_video_tags_";
+        List<String> redisKeys = new ArrayList<>();
+        for (Long videoId : videoIds) {
+            redisKeys.add(REDIS_PREFIX + String.valueOf(videoId));
+        }
+        List<String> videoTags = redisHelper.opsForValue().multiGet(redisKeys);
+        Map<Long, List<String>> videoTagDict = new HashMap<>();
+        if (videoTags != null) {
+            for (int i = 0; i < videoTags.size(); i++) {
+                String tagsStr = videoTags.get(i);
+                List<String> tags = new ArrayList<>();
+                if (tagsStr != null && !tagsStr.isEmpty()) {
+                    String[] tagsArray = tagsStr.split(",");
+                    tags = new ArrayList<>(Arrays.asList(tagsArray));
+                }
+                videoTagDict.put(videoIds.get(i), tags);
+            }
+        }
+        return videoTagDict;
+    }
+}

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4Density.java

@@ -11,7 +11,7 @@ import com.tzld.piaoquan.recommend.server.service.rank.RankService;
 import com.tzld.piaoquan.recommend.server.service.rank.extractor.RankExtractorFeature;
 import com.tzld.piaoquan.recommend.server.service.rank.processor.RankProcessorDensity;
 import com.tzld.piaoquan.recommend.server.service.rank.processor.RankProcessorTagFilter;
-import com.tzld.piaoquan.recommend.server.util.JSONUtils;
+import com.tzld.piaoquan.recommend.server.service.rank.extractor.RankExtractorItemTags;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.collections4.CollectionUtils;
 import org.apache.commons.lang3.RandomUtils;
@@ -21,132 +21,105 @@ import java.util.*;
 import java.util.stream.Collectors;
 
 /**
- * @author zhangbo
- * @desc 带密度控制的后处理 排序实验
+ * @author zhangbo sunxiaoyi
+ * @desc 后处理规则 + roc池flow池的合并
+ * 后处理参考文档：https://w42nne6hzg.feishu.cn/wiki/MYaGwCnF1iTFXUkSddAcA6CanFe
+ * roc池flow池的合并 文档： 暂无
  */
 @Service
 @Slf4j
 public class RankStrategy4Density extends RankService {
     @ApolloJsonValue("${RankStrategy4DensityFilter:}")
-    private Map<String,Map<String, Map<String, String>>> filterRules;
+    private Map<String,Map<String, Map<String, String>>> filterRules = new HashMap<>();
     @Override
-    public RankResult mergeAndSort(RankParam param, List<Video> rovRecallRank, List<Video> flowPoolRank) {
+    public RankResult mergeAndSort(RankParam param, List<Video> rovVideos, List<Video> flowVideos) {
 
-        if (CollectionUtils.isEmpty(rovRecallRank)) {
-            if (param.getSize() < flowPoolRank.size()) {
-                return new RankResult(flowPoolRank.subList(0, param.getSize()));
+         //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(flowPoolRank);
-            }
-        }
-        // 1 读取多样性密度控制规则------------------
-//        String appType = String.valueOf(param.getAppType());
-//        String ruleStr = this.redisTemplate.opsForValue().get("TAGS_FILTER_RULE_V1_JSON");
-//        Map<String, Integer> densityRules = new HashMap<>();
-//        if (ruleStr != null){
-//            Map<String, Map<String, Object>> ruleOrigin = JSONUtils.fromJson(ruleStr,
-//                    new TypeToken<Map<String, Map<String, Object>>>() {},
-//                    Collections.emptyMap());
-//            for (Map.Entry<String, Map<String, Object>> entry : ruleOrigin.entrySet()){
-//                String k = entry.getKey();
-//                if (!entry.getValue().containsKey(appType)){
-//                    continue;
-//                }
-//                JSONObject jb = (JSONObject) entry.getValue().get(appType);
-//                try{
-//                    if (jb.containsKey("density") && jb.get("density") instanceof Integer){
-//                        densityRules.put(k, jb.getInteger("density"));
-//                    }
-//                }catch (Exception e){
-//                    log.error("parse densityRules is wrong:", e);
-//                }
-//            }
-//        }
-        Map<String, Integer> densityRules = new HashMap<>();
-        String abCode = param.getAbCode();
-        if (this.filterRules != null && this.filterRules.containsKey(abCode)){
-            Map<String, Map<String, String>> rule = this.filterRules.get(abCode);
-            for (Map.Entry<String, Map<String, String>> entry : rule.entrySet()){
-                String key = entry.getKey();
-                Map<String, String> value = entry.getValue();
-                if (value.containsKey("density")){
-                    densityRules.put(key, Integer.valueOf(value.get("density")));
-                }
+                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));
 
-        // 2 读取video的tags------------------
-        List<Long> videoIds = new ArrayList<>();
-        for (Video v : rovRecallRank) {
-            videoIds.add(v.getVideoId());
-        }
-        for (Video v : flowPoolRank) {
-            videoIds.add(v.getVideoId());
-        }
-        Map<Long, List<String>> videoTagDict = RankExtractorFeature.getVideoTags(this.redisTemplate, videoIds);
-        for (Video v : rovRecallRank) {
-            v.setTags(videoTagDict.getOrDefault(v.getVideoId(), new ArrayList<>()));
+        //3 标签读取
+        if (rulesMap != null && !rulesMap.isEmpty()){
+            RankExtractorItemTags extractorItemTags = new RankExtractorItemTags(this.redisTemplate);
+            extractorItemTags.processor(rovVideos, flowVideos);
         }
-        for (Video v : flowPoolRank) {
-            v.setTags(videoTagDict.getOrDefault(v.getVideoId(), new ArrayList<>()));
+        //6 合并结果时间卡控
+        if (rulesMap != null && !rulesMap.isEmpty()){
+            RankProcessorTagFilter.tagFitlter(rovVideos, flowVideos, rulesMap);
         }
 
-        // 3 读取过滤规则，根据tag和过滤规则进行过滤------------------
-        if (this.filterRules != null && this.filterRules.containsKey(abCode)){
-            Map<String, Map<String, String>> rule = this.filterRules.get(abCode);
-            RankProcessorTagFilter.tagFitlter(rovRecallRank, flowPoolRank, rule);
-        }
+        //4 rov池提权功能
+
+        //5 rov池强插功能
 
-        // 4 流量池按比例强插---------------------
+        //7 流量池按比例强插
         List<Video> result = new ArrayList<>();
-        for (int i = 0; i < param.getTopK() && i < rovRecallRank.size(); i++) {
-            result.add(rovRecallRank.get(i));
+        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);
             log.info("rand={}, flowPoolP={}", rand, flowPoolP);
             if (rand < flowPoolP) {
-                if (flowPoolIndex < flowPoolRank.size()) {
-                    result.add(flowPoolRank.get(flowPoolIndex++));
+                if (flowPoolIndex < flowVideos.size()) {
+                    result.add(flowVideos.get(flowPoolIndex++));
                 } else {
                     break;
                 }
             } else {
-                if (rovPoolIndex < rovRecallRank.size()) {
-                    result.add(rovRecallRank.get(rovPoolIndex++));
+                if (rovPoolIndex < rovVideos.size()) {
+                    result.add(rovVideos.get(rovPoolIndex++));
                 } else {
                     break;
                 }
             }
         }
-        if (rovPoolIndex >= rovRecallRank.size()) {
-            for (int i = flowPoolIndex; i < flowPoolRank.size() && result.size() < param.getSize(); i++) {
-                result.add(flowPoolRank.get(i));
+        if (rovPoolIndex >= rovVideos.size()) {
+            for (int i = flowPoolIndex; i < flowVideos.size() && result.size() < param.getSize(); i++) {
+                result.add(flowVideos.get(i));
             }
         }
-        if (flowPoolIndex >= flowPoolRank.size()) {
-            for (int i = rovPoolIndex; i < rovRecallRank.size() && result.size() < param.getSize(); i++) {
-                result.add(rovRecallRank.get(i));
+        if (flowPoolIndex >= flowVideos.size()) {
+            for (int i = rovPoolIndex; i < rovVideos.size() && result.size() < param.getSize(); i++) {
+                result.add(rovVideos.get(i));
             }
         }
 
-        // 3 进行密度控制------------------
+        //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(r-> r.getVideoId()).collect(Collectors.toSet());
-        List<Video> rovRecallRankNew = rovRecallRank.stream().filter(r -> !videosSet.contains(r.getVideoId())).collect(Collectors.toList());
-        List<Video> flowPoolRankNew = flowPoolRank.stream().filter(r -> !videosSet.contains(r.getVideoId())).collect(Collectors.toList());
+        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> resultWithDnsity = RankProcessorDensity.mergeDensityControl(result,
                 rovRecallRankNew, flowPoolRankNew, densityRules);
-//        log.info("zhangbo22 old={}, new={}", JSONUtils.toJson(result),
-//                    JSONUtils.toJson(resultWithDnsity));
+
         return new RankResult(resultWithDnsity);
     }
 
+}
+
+
 
 
 //    public Video getTestVideo(Long id, String s){
@@ -156,6 +129,26 @@ public class RankStrategy4Density extends RankService {
 //        return a1;
 //    }
 
-
-
-}
+// 1 读取多样性密度控制规则------------------
+//        String appType = String.valueOf(param.getAppType());
+//        String ruleStr = this.redisTemplate.opsForValue().get("TAGS_FILTER_RULE_V1_JSON");
+//        Map<String, Integer> densityRules = new HashMap<>();
+//        if (ruleStr != null){
+//            Map<String, Map<String, Object>> ruleOrigin = JSONUtils.fromJson(ruleStr,
+//                    new TypeToken<Map<String, Map<String, Object>>>() {},
+//                    Collections.emptyMap());
+//            for (Map.Entry<String, Map<String, Object>> entry : ruleOrigin.entrySet()){
+//                String k = entry.getKey();
+//                if (!entry.getValue().containsKey(appType)){
+//                    continue;
+//                }
+//                JSONObject jb = (JSONObject) entry.getValue().get(appType);
+//                try{
+//                    if (jb.containsKey("density") && jb.get("density") instanceof Integer){
+//                        densityRules.put(k, jb.getInteger("density"));
+//                    }
+//                }catch (Exception e){
+//                    log.error("parse densityRules is wrong:", e);
+//                }
+//            }
+//        }

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV3.java

@@ -2,19 +2,26 @@ package com.tzld.piaoquan.recommend.server.service.rank.strategy;
 
 
 import com.alibaba.fastjson.JSONObject;
+import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
 import com.google.common.reflect.TypeToken;
+import com.tzld.piaoquan.recommend.feature.domain.video.base.UserFeature;
 import com.tzld.piaoquan.recommend.server.common.base.RankItem;
+import com.tzld.piaoquan.recommend.server.common.enums.AppTypeEnum;
 import com.tzld.piaoquan.recommend.server.model.Video;
+import com.tzld.piaoquan.recommend.server.service.flowpool.FlowPoolConstants;
 import com.tzld.piaoquan.recommend.server.service.rank.RankParam;
 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.RankExtractorUserFeature;
+import com.tzld.piaoquan.recommend.server.service.recall.RecallResult;
 import com.tzld.piaoquan.recommend.server.service.recall.strategy.*;
+import com.tzld.piaoquan.recommend.server.service.score.ScoreParam;
 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.springframework.data.redis.connection.RedisConnectionFactory;
 import org.springframework.data.redis.connection.RedisStandaloneConfiguration;
 import org.springframework.data.redis.connection.jedis.JedisConnectionFactory;
@@ -33,8 +40,8 @@ import java.util.stream.Collectors;
 @Service
 @Slf4j
 public class RankStrategy4RegionMergeModelV3 extends RankService {
-//    @ApolloJsonValue("${video.model.weightv3:}")
-//    private Map<String, Double> mergeWeight;
+    @ApolloJsonValue("${rank.score.merge.weight:}")
+    private Map<String, Double> mergeWeight;
     final private String CLASS_NAME = this.getClass().getSimpleName();
     public void duplicate(Set<Long> setVideo, List<Video> videos){
         Iterator<Video> iterator = videos.iterator();
@@ -48,6 +55,51 @@ public class RankStrategy4RegionMergeModelV3 extends RankService {
         }
     }
     @Override
+    public List<Video> mergeAndRankFlowPoolRecall(RankParam param) {
+        List<Video> quickFlowPoolVideos = sortFlowPoolByThompson(param, FlowPoolConstants.QUICK_PUSH_FORM);
+        if (CollectionUtils.isNotEmpty(quickFlowPoolVideos)) {
+            return quickFlowPoolVideos;
+        } else {
+            return sortFlowPoolByThompson(param, FlowPoolConstants.PUSH_FORM);
+        }
+    }
+    public List<Video> sortFlowPoolByThompson(RankParam param, String pushFrom) {
+
+        //初始化 userid
+        UserFeature userFeature = new UserFeature();
+        userFeature.setMid(param.getMid());
+
+        // 初始化RankItem
+        Optional<RecallResult.RecallData> data = param.getRecallResult().getData().stream()
+                .filter(d -> d.getPushFrom().equals(pushFrom))
+                .findFirst();
+        List<Video> videoList = data.get().getVideos();
+        if (videoList == null) {
+            return Collections.emptyList();
+        }
+        List<RankItem> rankItems = new ArrayList<>();
+        for (int i = 0; i < videoList.size(); i++) {
+            RankItem rankItem = new RankItem(videoList.get(i));
+            rankItems.add(rankItem);
+        }
+
+        // 初始化上下文参数
+        ScoreParam scoreParam = convert(param);
+        List<RankItem> rovRecallScore = ScorerUtils.getScorerPipeline(ScorerUtils.FLOWPOOL_CONF)
+                .scoring(scoreParam, userFeature, rankItems);
+
+        if (rovRecallScore == null) {
+            return Collections.emptyList();
+        }
+
+        return CommonCollectionUtils.toList(rovRecallScore, i -> {
+            // hard code 将排序分数 赋值给video的sortScore
+            Video v = i.getVideo();
+            v.setSortScore(i.getScore());
+            return v;
+        });
+    }
+    @Override
     public List<Video> mergeAndRankRovRecall(RankParam param) {
         //-------------------地域内部融合+去重复-------------------
         List<Video> rovRecallRank = new ArrayList<>();
@@ -65,6 +117,8 @@ public class RankStrategy4RegionMergeModelV3 extends RankService {
         List<Video> v6 = extractAndSort(param, ReturnVideoRecallStrategy.PUSH_FORM);
         this.duplicate(setVideo, v5);
         this.duplicate(setVideo, v6);
+        List<Video> v7 = extractAndSort(param, FestivalRecallStrategyV1.PUSH_FORM);
+        this.duplicate(setVideo, v7);
 
 //        rovRecallRank.addAll(v1);
 //        rovRecallRank.addAll(v2);
@@ -79,6 +133,7 @@ public class RankStrategy4RegionMergeModelV3 extends RankService {
         rovRecallRank.addAll(v4.subList(0, Math.min(5, v4.size())));
         rovRecallRank.addAll(v5.subList(0, Math.min(10, v5.size())));
         rovRecallRank.addAll(v6.subList(0, Math.min(10, v6.size())));
+        rovRecallRank.addAll(v7.subList(0, Math.min(10, v7.size())));
 
         //-------------------排-------------------
         //-------------------序-------------------
@@ -143,14 +198,30 @@ public class RankStrategy4RegionMergeModelV3 extends RankService {
 
             Double allreturnsScore = calScoreWeight(allreturns);
             item.scoresMap.put("allreturnsScore", allreturnsScore);
+
+            // 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 alpha = this.mergeWeight.getOrDefault("alpha", 1.0);
+        double beta = this.mergeWeight.getOrDefault("beta", 1.0);
         for (RankItem item : items){
-            double score = item.scoresMap.getOrDefault("view2playScore", 0.0) *
-                    item.scoresMap.getOrDefault("play2shareScore", 0.0) *
-                    item.scoresMap.getOrDefault("share2returnScore", 0.0) *
-                    Math.log(1 + item.scoresMap.getOrDefault("allreturnsScore", 0.0));
+            double trendScore =  item.scoresMap.getOrDefault("trendScore", 0.0) > 0.0 ?
+                    item.scoresMap.getOrDefault("trendScore", 0.0) : 0.0;
+            double newVideoScore =  item.scoresMap.getOrDefault("newVideoScore", 0.0) > 0.0 ?
+                    item.scoresMap.getOrDefault("newVideoScore", 0.0) : 0.0;
+            double score = item.getScoreStr() *
+                    item.scoresMap.getOrDefault("share2returnScore", 0.0)
+                    + alpha * trendScore
+                    + beta * newVideoScore
+                    ;
             Video video = item.getVideo();
             video.setScore(score);
             video.setSortScore(score);
@@ -158,9 +229,34 @@ public class RankStrategy4RegionMergeModelV3 extends RankService {
             video.setScoresMap(item.getScoresMap());
             result.add(video);
         }
-        result.sort(Comparator.comparingDouble(o -> -o.getSortScore()));
+        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 > 8){
+            return 0.0;
+        }
+        double score = 1.0 / (existenceDays + 5.0);
+        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 calScoreWeight(List<Double> data){
         Double up = 0.0;
@@ -193,7 +289,7 @@ public class RankStrategy4RegionMergeModelV3 extends RankService {
     }
 
     public List<RankItem> model(List<Video> videos, RankParam param,
-                             List<String> rtFeaPart){
+                                List<String> rtFeaPart){
         List<RankItem> result = new ArrayList<>();
         if (videos.isEmpty()){
             return result;
@@ -282,7 +378,8 @@ public class RankStrategy4RegionMergeModelV3 extends RankService {
                 }
                 try{
                     vfMap = JSONUtils.fromJson(vF, new TypeToken<Map<String, String>>() {}, vfMap);
-                    rankItems.get(i).setItemBasicFeature(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();

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV4.java

@@ -2,6 +2,7 @@ package com.tzld.piaoquan.recommend.server.service.rank.strategy;
 
 
 import com.alibaba.fastjson.JSONObject;
+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;
@@ -33,8 +34,8 @@ import java.util.stream.Collectors;
 @Service
 @Slf4j
 public class RankStrategy4RegionMergeModelV4 extends RankService {
-//    @ApolloJsonValue("${video.model.weightv3:}")
-//    private Map<String, Double> mergeWeight;
+    @ApolloJsonValue("${rank.score.merge.weight:}")
+    private Map<String, Double> mergeWeight;
     final private String CLASS_NAME = this.getClass().getSimpleName();
     public void duplicate(Set<Long> setVideo, List<Video> videos){
         Iterator<Video> iterator = videos.iterator();
@@ -151,12 +152,14 @@ public class RankStrategy4RegionMergeModelV4 extends RankService {
         }
         // 3 融合公式
         List<Video> result = new ArrayList<>();
+        double alpha = this.mergeWeight.getOrDefault("alpha", 1.0);
         for (RankItem item : items){
             double trendScore =  item.scoresMap.getOrDefault("trendScore", 0.0) > 0.0 ?
                     item.scoresMap.getOrDefault("trendScore", 0.0) : 0.0;
             double score = item.getScoreStr() *
-                    item.scoresMap.getOrDefault("share2returnScore", 0.0) *
-                    (1.0 + trendScore);
+                    item.scoresMap.getOrDefault("share2returnScore", 0.0)
+                    + alpha * trendScore
+                    ;
             Video video = item.getVideo();
             video.setScore(score);
             video.setSortScore(score);

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV5.java

@@ -2,6 +2,7 @@ package com.tzld.piaoquan.recommend.server.service.rank.strategy;
 
 
 import com.alibaba.fastjson.JSONObject;
+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;
@@ -33,8 +34,8 @@ import java.util.stream.Collectors;
 @Service
 @Slf4j
 public class RankStrategy4RegionMergeModelV5 extends RankService {
-//    @ApolloJsonValue("${video.model.weightv3:}")
-//    private Map<String, Double> mergeWeight;
+    @ApolloJsonValue("${rank.score.merge.weight:}")
+    private Map<String, Double> mergeWeight;
     final private String CLASS_NAME = this.getClass().getSimpleName();
     public void duplicate(Set<Long> setVideo, List<Video> videos){
         Iterator<Video> iterator = videos.iterator();
@@ -155,14 +156,17 @@ public class RankStrategy4RegionMergeModelV5 extends RankService {
         }
         // 3 融合公式
         List<Video> result = new ArrayList<>();
+        double alpha = this.mergeWeight.getOrDefault("alpha", 1.0);
+        double beta = this.mergeWeight.getOrDefault("beta", 1.0);
         for (RankItem item : items){
             double trendScore =  item.scoresMap.getOrDefault("trendScore", 0.0) > 0.0 ?
                     item.scoresMap.getOrDefault("trendScore", 0.0) : 0.0;
             double newVideoScore =  item.scoresMap.getOrDefault("newVideoScore", 0.0) > 0.0 ?
                     item.scoresMap.getOrDefault("newVideoScore", 0.0) : 0.0;
             double score = item.getScoreStr() *
-                    item.scoresMap.getOrDefault("share2returnScore", 0.0) *
-                    (1.0 + trendScore) *  (1.0 + newVideoScore)
+                    item.scoresMap.getOrDefault("share2returnScore", 0.0)
+                    + alpha * trendScore
+                    + beta * newVideoScore
                     ;
             Video video = item.getVideo();
             video.setScore(score);
@@ -176,28 +180,27 @@ public class RankStrategy4RegionMergeModelV5 extends RankService {
     }
     public double calNewVideoScore(Map<String, String> itemBasicMap){
         double existenceDays = Double.valueOf(itemBasicMap.getOrDefault("existence_days", "30"));
-        if (existenceDays > 7){
+        if (existenceDays > 8){
             return 0.0;
         }
-        if (existenceDays < 1){
-            existenceDays = 1.0;
-        }
-        double score = 1.0 / (existenceDays + 10.0);
+        double score = 1.0 / (existenceDays + 5.0);
         return score;
     }
     public double calTrendScore(List<Double> data){
-        int cnt = 0;
         double sum = 0.0;
         int size = data.size();
         for (int i=0; i<size-4; ++i){
             sum += data.get(i) - data.get(i+4);
-            cnt ++;
         }
         if (sum * 10 > 0.6){
             sum = 0.6;
         }else{
             sum = sum * 10;
         }
+        if (sum > 0){
+            // 为了打断点
+            sum = sum;
+        }
         return sum;
     }
 

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV6.java

@@ -0,0 +1,486 @@
+package com.tzld.piaoquan.recommend.server.service.rank.strategy;
+
+
+import com.alibaba.fastjson.JSONObject;
+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.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.RankExtractorUserFeature;
+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.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;
+import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
+/**
+ * @author zhangbo
+ * @desc 地域召回融合
+ */
+@Service
+@Slf4j
+public class RankStrategy4RegionMergeModelV6 extends RankService {
+    @ApolloJsonValue("${rank.score.merge.weight:}")
+    private Map<String, Double> mergeWeight;
+    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) {
+        //-------------------地域内部融合+去重复-------------------
+        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);
+        Set<Long> setVideo = new HashSet<>();
+        this.duplicate(setVideo, v1);
+        this.duplicate(setVideo, v2);
+        this.duplicate(setVideo, v3);
+        this.duplicate(setVideo, v4);
+        //-------------------地域 sim returnv2 融合+去重复-------------------
+        List<Video> v5 = extractAndSort(param, SimHotVideoRecallStrategy.PUSH_FORM);
+        List<Video> v6 = extractAndSort(param, ReturnVideoRecallStrategy.PUSH_FORM);
+        this.duplicate(setVideo, v5);
+        this.duplicate(setVideo, v6);
+        List<Video> v7 = extractAndSort(param, FestivalRecallStrategyV1.PUSH_FORM);
+        this.duplicate(setVideo, v7);
+
+//        rovRecallRank.addAll(v1);
+//        rovRecallRank.addAll(v2);
+//        rovRecallRank.addAll(v3);
+//        rovRecallRank.addAll(v4);
+//        rovRecallRank.addAll(v5);
+//        rovRecallRank.addAll(v6);
+
+        rovRecallRank.addAll(v1.subList(0, Math.min(20, v1.size())));
+        rovRecallRank.addAll(v2.subList(0, Math.min(15, v2.size())));
+        rovRecallRank.addAll(v3.subList(0, Math.min(10, v3.size())));
+        rovRecallRank.addAll(v4.subList(0, Math.min(5, v4.size())));
+        rovRecallRank.addAll(v5.subList(0, Math.min(10, v5.size())));
+        rovRecallRank.addAll(v6.subList(0, Math.min(10, v6.size())));
+        rovRecallRank.addAll(v7.subList(0, Math.min(10, v7.size())));
+
+        //-------------------排-------------------
+        //-------------------序-------------------
+        //-------------------逻-------------------
+        //-------------------辑-------------------
+//        List<String> videoIdKeys = rovRecallRank.stream()
+//                .map(t -> param.getRankKeyPrefix() + t.getVideoId())
+//                .collect(Collectors.toList());
+//        List<String> videoScores = this.redisTemplate.opsForValue().multiGet(videoIdKeys);
+//        log.info("rank mergeAndRankRovRecall videoIdKeys={}, videoScores={}", JSONUtils.toJson(videoIdKeys),
+//                JSONUtils.toJson(videoScores));
+//        if (CollectionUtils.isNotEmpty(videoScores)
+//                && videoScores.size() == rovRecallRank.size()) {
+//            for (int i = 0; i < videoScores.size(); i++) {
+//                rovRecallRank.get(i).setSortScore(NumberUtils.toDouble(videoScores.get(i), 0.0));
+//            }
+//            Collections.sort(rovRecallRank, Comparator.comparingDouble(o -> -o.getSortScore()));
+//        }
+
+        // 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<>();
+        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> returns = getStaticData(itemRealMap, datehours, "p_return_uv_list_1h");
+            List<Double> allreturns = getStaticData(itemRealMap, datehours, "return_uv_list_1h");
+
+            List<Double> share2return = getRateData(returns, shares, 1.0, 1000.0);
+            Double share2returnScore = calScoreWeight(share2return);
+            List<Double> view2return = getRateData(returns, views, 1.0, 1000.0);
+            Double view2returnScore = calScoreWeight(view2return);
+            List<Double> view2play = getRateData(plays, views, 1.0, 1000.0);
+            Double view2playScore = calScoreWeight(view2play);
+            List<Double> play2share = getRateData(shares, plays, 1.0, 1000.0);
+            Double play2shareScore = calScoreWeight(play2share);
+            item.scoresMap.put("share2returnScore", share2returnScore);
+            item.scoresMap.put("view2returnScore", view2returnScore);
+            item.scoresMap.put("view2playScore", view2playScore);
+            item.scoresMap.put("play2shareScore", play2shareScore);
+
+            Double allreturnsScore = calScoreWeight(allreturns);
+            item.scoresMap.put("allreturnsScore", allreturnsScore);
+
+            // 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 alpha = this.mergeWeight.getOrDefault("alpha", 1.0);
+        double beta = this.mergeWeight.getOrDefault("beta", 1.0);
+        for (RankItem item : items){
+            double trendScore =  item.scoresMap.getOrDefault("trendScore", 0.0) > 0.0 ?
+                    item.scoresMap.getOrDefault("trendScore", 0.0) : 0.0;
+            double newVideoScore =  item.scoresMap.getOrDefault("newVideoScore", 0.0) > 0.0 ?
+                    item.scoresMap.getOrDefault("newVideoScore", 0.0) : 0.0;
+            double score = item.getScoreStr() *
+                    item.scoresMap.getOrDefault("share2returnScore", 0.0)
+                    + alpha * trendScore
+                    + beta * newVideoScore
+                    ;
+            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 > 8){
+            return 0.0;
+        }
+        double score = 1.0 / (existenceDays + 5.0);
+        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 calScoreWeight(List<Double> data){
+        Double up = 0.0;
+        Double down = 0.0;
+        for (int i=0; i<data.size(); ++i){
+            up += 1.0 / (i + 1) * data.get(i);
+            down += 1.0 / (i + 1);
+        }
+        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){
+            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));
+                }
+            }else{
+                JSONObject obj = new JSONObject();
+                obj.put("name", "user_key_in_model_is_null");
+                obj.put("class", this.CLASS_NAME);
+                log.info(obj.toString());
+//                return videos;
+            }
+        }
+        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);
+        log.info("userFeature in model = {}", JSONUtils.toJson(f1));
+
+        // 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);
+            }
+        }
+
+
+        log.info("ItemFeature = {}", JSONUtils.toJson(videoFeatures));
+
+
+
+        List<RankItem> rovRecallScore = ScorerUtils.getScorerPipeline(ScorerUtils.BASE_CONF)
+                .scoring(sceneFeatureMap, userFeatureMap, rankItems);
+        log.info("mergeAndRankRovRecallNew rovRecallScore={}", JSONUtils.toJson(rovRecallScore));
+        JSONObject obj = new JSONObject();
+        obj.put("name", "user_key_in_model_is_not_null");
+        obj.put("class", this.CLASS_NAME);
+        log.info(obj.toString());
+        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;
+    }
+
+}

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

@@ -86,7 +86,12 @@ public class RecallService implements ApplicationContextAware {
         if (param.getAppType() == AppTypeEnum.LAO_HAO_KAN_VIDEO.getCode()
                 || param.getAppType() == AppTypeEnum.ZUI_JING_QI.getCode()) {
             strategies.addAll(getRegionRecallStrategy(param));
-        } else {
+        } else if (param.getAppType() == AppTypeEnum.PIAO_QUAN_MEIHAO_ZHUFU.getCode()){
+            strategies.add(strategyMap.get(FestivalRecallStrategyV1.class.getSimpleName()));
+            strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV2.class.getSimpleName()));
+            strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV3.class.getSimpleName()));
+            return strategies;
+        }else {
             switch (abCode) {
                 case "60120": // 576
                 case "60121": // 536
@@ -94,6 +99,7 @@ public class RecallService implements ApplicationContextAware {
                 case "60123": // 541
                 case "60124": // 546
                 case "60125": // 547
+                case "60126": // 548
                     strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV1.class.getSimpleName()));
                     strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV2.class.getSimpleName()));
                     strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV3.class.getSimpleName()));
@@ -150,12 +156,17 @@ public class RecallService implements ApplicationContextAware {
                 case "60120": // 576
                 case "60121": // 536
                 case "60122": // 537
-                case "60123": // 541
                 case "60124": // 546
                 case "60125": // 547
                     strategies.add(strategyMap.get(SimHotVideoRecallStrategy.class.getSimpleName()));
                     strategies.add(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
                     break;
+                case "60123": // 541
+                case "60126": // 548
+                    strategies.add(strategyMap.get(SimHotVideoRecallStrategy.class.getSimpleName()));
+                    strategies.add(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
+                    strategies.add(strategyMap.get(FestivalRecallStrategyV1.class.getSimpleName()));
+                    break;
                 case "60104": // 去掉sim的对比实验
                     strategies.add(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
                     break;