568增加优质老内容召回策略

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

@@ -2,8 +2,6 @@ package com.tzld.piaoquan.recommend.server.service.rank;
 
 import com.tzld.piaoquan.recommend.server.service.ServiceBeanFactory;
 import com.tzld.piaoquan.recommend.server.service.rank.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.springframework.beans.factory.annotation.Autowired;
@@ -28,6 +26,7 @@ public class RankRouter {
         STRATEGY_CLASSES.put("566", RankStrategy4RegionMergeModelV566.class);
         STRATEGY_CLASSES.put("567", RankStrategy4RegionMergeModelV567.class);
         STRATEGY_CLASSES.put("569", RankStrategy4RegionMergeModelV569.class);
+        STRATEGY_CLASSES.put("568", RankStrategy4RegionMergeModelV568.class);
     }
 
     @Autowired

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV568.java

@@ -0,0 +1,353 @@
+package com.tzld.piaoquan.recommend.server.service.rank.strategy;
+
+import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
+import com.tzld.piaoquan.recommend.server.common.ThreadPoolFactory;
+import com.tzld.piaoquan.recommend.server.common.base.RankItem;
+import com.tzld.piaoquan.recommend.server.model.Video;
+import com.tzld.piaoquan.recommend.server.service.FeatureService;
+import com.tzld.piaoquan.recommend.server.service.rank.RankParam;
+import com.tzld.piaoquan.recommend.server.service.rank.extractor.ExtractorUtils;
+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 lombok.extern.slf4j.Slf4j;
+import org.apache.commons.collections4.MapUtils;
+import org.apache.commons.math3.util.Pair;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.stereotype.Service;
+
+import java.util.*;
+import java.util.concurrent.Future;
+import java.util.concurrent.TimeUnit;
+import java.util.stream.Collectors;
+
+@Service
+@Slf4j
+public class RankStrategy4RegionMergeModelV568 extends RankStrategy4RegionMergeModelBasic {
+    @ApolloJsonValue("${rank.score.merge.weightv568:}")
+    private Map<String, Double> mergeWeight;
+
+    @Autowired
+    private FeatureService featureService;
+
+    @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();
+        List<Video> v0 = oldRovs.size() <= sizeReturn
+                ? oldRovs
+                : oldRovs.subList(0, sizeReturn);
+        Set<Long> setVideo = new HashSet<>();
+        this.duplicate(setVideo, v0);
+        setVideo.addAll(v0.stream().map(Video::getVideoId).collect(Collectors.toSet()));
+        List<Video> rovRecallRank = new ArrayList<>(v0);
+        //-------------------return相似召回 改为 优质老内容召回------------------
+        List<Video> v6 = extractAndSort(param, RegionRealtimeRecallStrategyV7LongTermV1.PUSH_FORM);
+        v6 = v6.stream().filter(r -> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
+        v6 = v6.subList(0, Math.min(mergeWeight.getOrDefault("v6", 10.0).intValue(), v6.size()));
+        rovRecallRank.addAll(v6);
+        setVideo.addAll(v6.stream().map(Video::getVideoId).collect(Collectors.toSet()));
+        //-------------------新地域召回------------------
+        List<Video> v1 = extractAndSort(param, RegionRealtimeRecallStrategyV1.PUSH_FORM);
+        v1 = v1.stream().filter(r -> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
+        v1 = v1.subList(0, Math.min(mergeWeight.getOrDefault("v1", 5.0).intValue(), v1.size()));
+        rovRecallRank.addAll(v1);
+        setVideo.addAll(v1.stream().map(Video::getVideoId).collect(Collectors.toSet()));
+        //-------------------scene cf rovn------------------
+        List<Video> sceneCFRovn = extractAndSort(param, SceneCFRovnRecallStrategy.PUSH_FORM);
+        sceneCFRovn = sceneCFRovn.stream().filter(r -> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
+        sceneCFRovn = sceneCFRovn.subList(0, Math.min(mergeWeight.getOrDefault("sceneCFRovn", 5.0).intValue(), sceneCFRovn.size()));
+        rovRecallRank.addAll(sceneCFRovn);
+        setVideo.addAll(sceneCFRovn.stream().map(Video::getVideoId).collect(Collectors.toSet()));
+        //-------------------scene cf rosn------------------
+        List<Video> sceneCFRosn = extractAndSort(param, SceneCFRosnRecallStrategy.PUSH_FORM);
+        sceneCFRosn = sceneCFRosn.stream().filter(r -> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
+        sceneCFRosn = sceneCFRosn.subList(0, Math.min(mergeWeight.getOrDefault("sceneCFRosn", 5.0).intValue(), sceneCFRosn.size()));
+        rovRecallRank.addAll(sceneCFRosn);
+        setVideo.addAll(sceneCFRosn.stream().map(Video::getVideoId).collect(Collectors.toSet()));
+
+        //-------------------排-------------------
+        //-------------------序-------------------
+        //-------------------逻-------------------
+        //-------------------辑-------------------
+
+        // 1 批量获取特征  省份参数要对齐  headvid  要传递过来！
+        List<String> vids = CommonCollectionUtils.toListDistinct(rovRecallRank, v -> String.valueOf(v.getVideoId()));
+
+        // k1:视频、k2:表、k3:特征、v:特征值
+        String provinceCn = param.getProvince().replaceAll("省$", "");
+        String headVid = String.valueOf(param.getHeadVid());
+        FeatureService.Feature feature = featureService.getFeature(param.getMid(), vids,
+                String.valueOf(param.getAppType()), provinceCn, headVid);
+        Map<String, Map<String, String>> featureOriginUser = feature.getUserFeature();
+        Map<String, Map<String, Map<String, String>>> featureOriginVideo = feature.getVideoFeature();
+
+
+        // 2 特征处理
+        Map<String, Double> userFeatureMapDouble = new HashMap<>();
+        String mid = param.getMid();
+        Map<String, String> c1 = featureOriginUser.getOrDefault("alg_mid_feature_play", new HashMap<>());
+        Map<String, String> c2 = featureOriginUser.getOrDefault("alg_mid_feature_share_and_return", new HashMap<>());
+        Map<String, String> c3 = featureOriginUser.getOrDefault("alg_mid_feature_play_tags", new HashMap<>());
+        Map<String, String> c4 = featureOriginUser.getOrDefault("alg_mid_feature_return_tags", new HashMap<>());
+        Map<String, String> c5 = featureOriginUser.getOrDefault("alg_mid_feature_share_tags", new HashMap<>());
+        Map<String, String> c6 = featureOriginUser.getOrDefault("alg_mid_feature_feed_exp_share_tags_v2", new HashMap<>());
+        Map<String, String> c7 = featureOriginUser.getOrDefault("alg_mid_feature_feed_exp_return_tags_v2", new HashMap<>());
+        Map<String, String> c8 = featureOriginUser.getOrDefault("alg_mid_feature_sharecf", new HashMap<>());
+        Map<String, String> c9 = featureOriginUser.getOrDefault("alg_mid_feature_returncf", new HashMap<>());
+
+        if (!c1.isEmpty()) {
+            userFeatureMapDouble.put("playcnt_6h", Double.parseDouble(c1.getOrDefault("playcnt_6h", "0")));
+            userFeatureMapDouble.put("playcnt_1d", Double.parseDouble(c1.getOrDefault("playcnt_1d", "0")));
+            userFeatureMapDouble.put("playcnt_3d", Double.parseDouble(c1.getOrDefault("playcnt_3d", "0")));
+            userFeatureMapDouble.put("playcnt_7d", Double.parseDouble(c1.getOrDefault("playcnt_7d", "0")));
+        }
+        if (!c2.isEmpty()) {
+            userFeatureMapDouble.put("share_pv_12h", Double.parseDouble(c2.getOrDefault("share_pv_12h", "0")));
+            userFeatureMapDouble.put("share_pv_1d", Double.parseDouble(c2.getOrDefault("share_pv_1d", "0")));
+            userFeatureMapDouble.put("share_pv_3d", Double.parseDouble(c2.getOrDefault("share_pv_3d", "0")));
+            userFeatureMapDouble.put("share_pv_7d", Double.parseDouble(c2.getOrDefault("share_pv_7d", "0")));
+            userFeatureMapDouble.put("return_uv_12h", Double.parseDouble(c2.getOrDefault("return_uv_12h", "0")));
+            userFeatureMapDouble.put("return_uv_1d", Double.parseDouble(c2.getOrDefault("return_uv_1d", "0")));
+            userFeatureMapDouble.put("return_uv_3d", Double.parseDouble(c2.getOrDefault("return_uv_3d", "0")));
+            userFeatureMapDouble.put("return_uv_7d", Double.parseDouble(c2.getOrDefault("return_uv_7d", "0")));
+        }
+
+        Map<String, String> c34567Map = new HashMap<>(15);
+        List<Tuple2> tmpList0 = Arrays.asList(
+                new Tuple2(c3, "c3_feature"),
+                new Tuple2(c4, "c4_feature"),
+                new Tuple2(c5, "c5_feature"),
+                new Tuple2(c6, "c6_feature"),
+                new Tuple2(c7, "c7_feature")
+        );
+        for (Tuple2 tuple2 : tmpList0) {
+            for (String key_time : Arrays.asList("tags_1d", "tags_3d", "tags_7d")) {
+                String tags = tuple2.first.getOrDefault(key_time, "");
+                if (!tags.isEmpty()) {
+                    c34567Map.put(tuple2.name + "_" + key_time, tags);
+                }
+            }
+        }
+
+        Map<String, Map<String, String[]>> c89Map = new HashMap<>(4);
+        List<Tuple2> tmpList1 = Arrays.asList(
+                new Tuple2(c8, "c8_feature"),
+                new Tuple2(c9, "c9_feature")
+        );
+        for (Tuple2 tuple2 : tmpList1) {
+            for (String key_action : Arrays.asList("share", "return")) {
+                String cfListStr = tuple2.first.getOrDefault(key_action, "");
+                if (!cfListStr.isEmpty()) {
+                    Map<String, String[]> cfMap = new HashMap<>();
+                    String[] entries = cfListStr.split(",");
+                    for (String entry : entries) {
+                        String[] rList = entry.split(":");
+                        if (rList.length >= 4) { // 确保分割后有四个元素
+                            String key = rList[0];
+                            String value1 = rList[1];
+                            String value2 = rList[2];
+                            String value3 = rList[3];
+                            String[] strs = {value1, value2, value3};
+                            cfMap.put(key, strs);
+                        }
+                    }
+                    c89Map.put(tuple2.name + "_" + key_action, cfMap);
+                }
+            }
+        }
+
+
+        List<RankItem> rankItems = CommonCollectionUtils.toList(rovRecallRank, RankItem::new);
+        for (RankItem item : rankItems) {
+            Map<String, Double> featureMap = new HashMap<>();
+            String vid = item.getVideoId() + "";
+            Map<String, String> b1 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_all_exp_v2", new HashMap<>());
+            Map<String, String> b2 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_all_share", new HashMap<>());
+            Map<String, String> b3 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_all_return", new HashMap<>());
+            Map<String, String> b6 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_exp2share_v2", new HashMap<>());
+            Map<String, String> b7 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_share2return", new HashMap<>());
+
+            Map<String, String> b8 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_noflow_exp_v2", new HashMap<>());
+            Map<String, String> b9 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_noflow_root_share_v2", new HashMap<>());
+            Map<String, String> b10 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_noflow_root_return_v2", new HashMap<>());
+            Map<String, String> b11 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_flow_exp_v2", new HashMap<>());
+            Map<String, String> b12 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_flow_root_share_v2", new HashMap<>());
+            Map<String, String> b13 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_flow_root_return_v2", new HashMap<>());
+            Map<String, String> b17 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_province_exp_v2", new HashMap<>());
+            Map<String, String> b18 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_province_root_share_v2", new HashMap<>());
+            Map<String, String> b19 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_province_root_return_v2", new HashMap<>());
+
+            List<Tuple4> originData = Arrays.asList(
+                    new Tuple4(b1, b2, b3, "b123"),
+                    new Tuple4(b1, b6, b7, "b167"),
+                    new Tuple4(b8, b9, b10, "b8910"),
+                    new Tuple4(b11, b12, b13, "b111213"),
+                    new Tuple4(b17, b18, b19, "b171819")
+            );
+
+            for (Tuple4 tuple4 : originData) {
+                for (String prefix2 : Arrays.asList("1h", "2h", "3h", "4h", "12h", "1d", "3d", "7d")) {
+                    double exp = tuple4.first.isEmpty() ? 0 : Double.parseDouble(tuple4.first.getOrDefault("exp_pv_" + prefix2, "0.0"));
+                    double share = tuple4.second.isEmpty() ? 0 : Double.parseDouble(tuple4.second.getOrDefault("share_pv_" + prefix2, "0.0"));
+                    double returns = tuple4.third.isEmpty() ? 0 : Double.parseDouble(tuple4.third.getOrDefault("return_uv_" + prefix2, "0.0"));
+
+                    double f1 = ExtractorUtils.calDiv(share, exp);
+                    double f2 = ExtractorUtils.calLog(share);
+                    double f3 = ExtractorUtils.calDiv(returns, exp);
+                    double f4 = ExtractorUtils.calLog(returns);
+                    double f5 = f3 * f4;
+
+                    String key1 = tuple4.name + "_" + prefix2 + "_" + "STR";
+                    String key2 = tuple4.name + "_" + prefix2 + "_" + "log(share)";
+                    String key3 = tuple4.name + "_" + prefix2 + "_" + "ROV";
+                    String key4 = tuple4.name + "_" + prefix2 + "_" + "log(return)";
+                    String key5 = tuple4.name + "_" + prefix2 + "_" + "ROV*log(return)";
+
+                    featureMap.put(key1, f1);
+                    featureMap.put(key2, f2);
+                    featureMap.put(key3, f3);
+                    featureMap.put(key4, f4);
+                    featureMap.put(key5, f5);
+                }
+            }
+
+            Map<String, String> videoInfo = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_basic_info", new HashMap<>());
+            featureMap.put("total_time", Double.parseDouble(videoInfo.getOrDefault("total_time", "0")));
+            featureMap.put("bit_rate", Double.parseDouble(videoInfo.getOrDefault("bit_rate", "0")));
+
+            String title = videoInfo.getOrDefault("title", "");
+            if (!title.isEmpty()) {
+                List<Future<Pair<String, Double[]>>> futures = new ArrayList<>();
+                for (String name : Arrays.asList("c3_feature", "c4_feature", "c5_feature", "c6_feature", "c7_feature")) {
+                    for (String key_time : Arrays.asList("tags_1d", "tags_3d", "tags_7d")) {
+                        String key = name + "_" + key_time;
+                        String tags = c34567Map.getOrDefault(key, "");
+                        if (!tags.isEmpty()) {
+                            Future<Pair<String, Double[]>> future = ThreadPoolFactory.defaultPool().submit(() -> {
+                                Double[] doubles = null;
+                                if (param.getAbExpCodes().contains(word2vecExp)) {
+                                    doubles = ExtractorUtils.funcC34567ForTagsNew(tags, title);
+                                } else {
+                                    doubles = ExtractorUtils.funcC34567ForTags(tags, title);
+                                }
+                                return Pair.create(key, doubles);
+                            });
+                            futures.add(future);
+                        }
+                    }
+                }
+                try {
+                    for (Future<Pair<String, Double[]>> future : futures) {
+                        Pair<String, Double[]> pair = future.get(1000, TimeUnit.MILLISECONDS);
+                        featureMap.put(pair.getFirst() + "_matchnum", pair.getSecond()[0]);
+                        featureMap.put(pair.getFirst() + "_maxscore", pair.getSecond()[1]);
+                        featureMap.put(pair.getFirst() + "_avgscore", pair.getSecond()[2]);
+                    }
+                } catch (Exception e) {
+                    log.error("concurrent similarity error", e);
+                }
+            }
+
+            if (!vid.isEmpty()) {
+                for (String key_feature : Arrays.asList("c8_feature", "c9_feature")) {
+                    for (String key_action : Arrays.asList("share", "return")) {
+                        Map<String, String[]> cfMap = c89Map.getOrDefault(key_feature + "_" + key_action, new HashMap<>());
+                        if (cfMap.containsKey(vid)) {
+                            String[] scores = cfMap.get(vid);
+                            Double score1 = Double.parseDouble(scores[0]);
+                            Double score2 = Double.parseDouble(scores[1]);
+                            Double score3 = Double.parseDouble(scores[2]) <= 0 ? 0D : 1.0 / Double.parseDouble(scores[2]);
+                            featureMap.put(key_feature + "_" + key_action + "_score", score1);
+                            featureMap.put(key_feature + "_" + key_action + "_num", score2);
+                            featureMap.put(key_feature + "_" + key_action + "_rank", score3);
+                        }
+                    }
+                }
+            }
+            Map<String, String> d1 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_recsys_feature_cf_i2i_new_v2", new HashMap<>());
+            if (!d1.isEmpty()) {
+                featureMap.put("d1_exp", Double.parseDouble(d1.getOrDefault("exp", "0")));
+                featureMap.put("d1_return_n", Double.parseDouble(d1.getOrDefault("return_n", "0")));
+                featureMap.put("d1_rovn", Double.parseDouble(d1.getOrDefault("rovn", "0")));
+            }
+            item.featureMapDouble = featureMap;
+        }
+
+        // 3 连续值特征分桶
+        readBucketFile();
+        Map<String, String> userFeatureMap = new HashMap<>(userFeatureMapDouble.size());
+        for (Map.Entry<String, Double> entry : userFeatureMapDouble.entrySet()) {
+            String name = entry.getKey();
+            Double score = entry.getValue();
+            // 注意：0值、不在分桶文件中的特征，会被过滤掉。
+            if (score > 1E-8 && this.bucketsLen.containsKey(name) && this.bucketsMap.containsKey(name)) {
+                Double bucketNum = this.bucketsLen.get(name);
+                double[] buckets = this.bucketsMap.get(name);
+                Double scoreNew = 1.0 / bucketNum * (ExtractorUtils.findInsertPosition(buckets, score) + 1.0);
+                userFeatureMap.put(name, String.valueOf(scoreNew));
+            }
+        }
+        for (RankItem item : rankItems) {
+            Map<String, String> featureMap = new HashMap<>();
+            Map<String, Double> featureMapDouble = item.featureMapDouble;
+
+            for (Map.Entry<String, Double> entry : featureMapDouble.entrySet()) {
+                String name = entry.getKey();
+                Double score = entry.getValue();
+                // 注意：0值、不在分桶文件中的特征，会被过滤掉。
+                if (score > 1E-8 && this.bucketsLen.containsKey(name) && this.bucketsMap.containsKey(name)) {
+                    Double bucketNum = this.bucketsLen.get(name);
+                    double[] buckets = this.bucketsMap.get(name);
+                    Double scoreNew = 1.0 / bucketNum * (ExtractorUtils.findInsertPosition(buckets, score) + 1.0);
+                    featureMap.put(name, String.valueOf(scoreNew));
+                }
+            }
+            item.featureMap = featureMap;
+        }
+        // 4 排序模型计算
+        Map<String, String> sceneFeatureMap = new HashMap<>(0);
+        List<RankItem> items = ScorerUtils.getScorerPipeline("feeds_score_config_20240807.conf").scoring(sceneFeatureMap, userFeatureMap, rankItems);
+        // 5 排序公式特征
+        Map<String, Map<String, String>> vid2MapFeature = this.getVideoRedisFeature(vids, "redis:vid_hasreturn_vor:");
+        List<Video> result = new ArrayList<>();
+        for (RankItem item : items) {
+            double score;
+            double fmRovOrigin = item.getScoreRov();
+            item.getScoresMap().put("fmRovOrigin", fmRovOrigin);
+            double fmRov = restoreScore(fmRovOrigin);
+            item.getScoresMap().put("fmRov", fmRov);
+            double hasReturnRovScore = Double.parseDouble(vid2MapFeature.getOrDefault(item.getVideoId() + "", new HashMap<>()).getOrDefault("rov", "0"));
+            item.getScoresMap().put("hasReturnRovScore", hasReturnRovScore);
+            double vor = Double.parseDouble(vid2MapFeature.getOrDefault(item.getVideoId() + "", new HashMap<>()).getOrDefault("vor", "0"));
+            item.getScoresMap().put("vor", vor);
+            score = fmRov * (0.1 + hasReturnRovScore) * (0.1 + vor);
+            Video video = item.getVideo();
+            video.setScore(score);
+            video.setSortScore(score);
+            video.setScoresMap(item.getScoresMap());
+            video.setAllFeatureMap(item.getAllFeatureMap());
+            if (MapUtils.isNotEmpty(feature.getVideoFeature()) && MapUtils.isNotEmpty(feature.getVideoFeature().get(item.getVideoId() + ""))) {
+                video.getMetaFeatureMap().putAll(feature.getVideoFeature().get(item.getVideoId() + ""));
+            }
+            if (MapUtils.isNotEmpty(feature.getUserFeature())) {
+                video.getMetaFeatureMap().putAll(feature.getUserFeature());
+            }
+            result.add(video);
+        }
+        result.sort(Comparator.comparingDouble(o -> -o.getSortScore()));
+        return result;
+    }
+}

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RegionMergeModelV569.java

@@ -16,7 +16,6 @@ import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.collections4.MapUtils;
 import org.apache.commons.math3.util.Pair;
 import org.springframework.beans.factory.annotation.Autowired;
-import org.springframework.beans.factory.annotation.Value;
 import org.springframework.stereotype.Service;
 
 import java.util.*;

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

@@ -106,14 +106,12 @@ public class RecallService implements ApplicationContextAware {
         strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV1.class.getSimpleName()));
         strategies.addAll(getRegionRecallStrategy(param));
         strategies.add(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
-
+        strategies.add(strategyMap.get(SceneCFRovnRecallStrategy.class.getSimpleName()));
+        strategies.add(strategyMap.get(SceneCFRosnRecallStrategy.class.getSimpleName()));
         Set<String> abExpCodes = param.getAbExpCodes();
-        if (CollectionUtils.isNotEmpty(abExpCodes)) {
-            if (abExpCodes.contains("563") || abExpCodes.contains("562")
-                    || abExpCodes.contains("564") || abExpCodes.contains("567")) {
-                strategies.add(strategyMap.get(SceneCFRovnRecallStrategy.class.getSimpleName()));
-                strategies.add(strategyMap.get(SceneCFRosnRecallStrategy.class.getSimpleName()));
-            }
+        if (abExpCodes.contains("568")){
+            strategies.remove(strategyMap.get(ReturnVideoRecallStrategy.class.getSimpleName()));
+            strategies.add(strategyMap.get(RegionRealtimeRecallStrategyV7LongTermV1.class.getSimpleName()));
         }
 
         // 命中用户黑名单不走流量池

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/recall/strategy/RegionRealtimeRecallStrategyV7LongTermV1.java

@@ -0,0 +1,66 @@
+package com.tzld.piaoquan.recommend.server.service.recall.strategy;
+
+import com.google.common.collect.Lists;
+import com.tzld.piaoquan.recommend.server.model.Video;
+import com.tzld.piaoquan.recommend.server.service.filter.FilterParam;
+import com.tzld.piaoquan.recommend.server.service.filter.FilterResult;
+import com.tzld.piaoquan.recommend.server.service.filter.FilterService;
+import com.tzld.piaoquan.recommend.server.service.recall.FilterParamFactory;
+import com.tzld.piaoquan.recommend.server.service.recall.RecallParam;
+import com.tzld.piaoquan.recommend.server.service.recall.RecallStrategy;
+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.Autowired;
+import org.springframework.stereotype.Component;
+
+import java.util.*;
+
+/**
+ * @author zhangbo
+ */
+@Component
+public class RegionRealtimeRecallStrategyV7LongTermV1 implements RecallStrategy {
+    @Autowired
+    private FilterService filterService;
+
+    @Override
+    public List<Video> recall(RecallParam param) {
+        Map<String, String> param4Model = new HashMap<>(1);
+        param4Model.put("t_2_8", "5");
+        param4Model.put("t_9_36", "10");
+        param4Model.put("t_37_90", "15");
+        param4Model.put("t_91_365", "20");
+        ScorerPipeline4Recall pipeline = ScorerUtils.getScorerPipeline4Recall("feeds_recall_config_region_v7_longterm.conf");
+        List<List<Pair<Long, Double>>> results = pipeline.recall(param4Model);
+        List<Pair<Long, Double>> result = results.get(0);
+        for (int i = 1; i < results.size(); ++i) {
+            result.addAll(results.get(i));
+        }
+        Map<Long, Double> videoMap = new LinkedHashMap<>();
+        for (Pair<Long, Double> v : result) {
+            videoMap.put(v.getLeft(), v.getRight());
+        }
+        FilterParam filterParam = FilterParamFactory.create(param, Lists.newArrayList(videoMap.keySet()));
+        FilterResult filterResult = filterService.filter(filterParam);
+        List<Video> videosResult = new ArrayList<>();
+        if (filterResult != null && CollectionUtils.isNotEmpty(filterResult.getVideoIds())) {
+            filterResult.getVideoIds().forEach(vid -> {
+                Video video = new Video();
+                video.setVideoId(vid);
+                video.setRovScore(videoMap.get(vid));
+                video.setPushFrom(pushFrom());
+                videosResult.add(video);
+            });
+        }
+        videosResult.sort(Comparator.comparingDouble(o -> -o.getRovScore()));
+        return videosResult;
+    }
+    public static final String PUSH_FORM = "recall_strategy_longterm_v1";
+    @Override
+    public String pushFrom(){
+        return PUSH_FORM;
+    }
+
+}

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

@@ -40,6 +40,7 @@ public final class ScorerUtils {
         ScorerUtils.init4Recall("feeds_recall_config_region_v1.conf");
         ScorerUtils.init4Recall("feeds_score_config_bless.conf");
         ScorerUtils.init4Recall("feeds_recall_config_tomson.conf");
+        ScorerUtils.init4Recall("feeds_recall_config_region_v7_longterm.conf");
         List<String> bucketFileList = Arrays.asList("20241209_rov_bucket.txt", "20241209_nor_bucket.txt");
         FeatureBucketUtils.init(bucketFileList);
     }

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/score4recall/model4recall/Model4RecallLongTerm.java

@@ -0,0 +1,50 @@
+package com.tzld.piaoquan.recommend.server.service.score4recall.model4recall;
+import org.apache.commons.lang3.tuple.Pair;
+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 Model4RecallLongTerm extends AbstractModel {
+    private static final Logger LOGGER = LoggerFactory.getLogger(Model4RecallLongTerm.class);
+    public Map<String, List<Pair<Long, Double>>> kv;
+    public Model4RecallLongTerm() {
+        this.kv = new HashMap<>();
+    }
+
+    @Override
+    public boolean loadFromStream(InputStreamReader in) throws IOException {
+        BufferedReader input = new BufferedReader(in);
+        String line = null;
+        while ((line = input.readLine()) != null) {
+            String[] items = line.split("\t");
+            if (items.length < 3) {
+                continue;
+            }
+            String key = items[0].trim();
+            long l1 = 0L;
+            double l2 = 0D;
+            try{
+                l1 = Long.parseLong(items[1]);
+                l2 = Double.parseDouble(items[2]);
+            }catch (Exception e){
+                LOGGER.error(String.format("something is wrong with parse pair in Model4RecallLongTerm %s %s: ", items[1], items[2]), e);
+                continue;
+            }
+            List<Pair<Long, Double>> list = this.kv.getOrDefault(key, new ArrayList<>());
+            list.add(Pair.of(l1, l2));
+            this.kv.put(key, list);
+        }
+        input.close();
+        in.close();
+        kv.forEach((key, value) ->
+                value.sort((p1, p2) -> Double.compare(p2.getRight(), p1.getRight()))
+        );
+        return true;
+    }
+}

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/score4recall/strategy/RegionRecallScorerV7LongTerm.java

@@ -0,0 +1,70 @@
+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.Model4RecallLongTerm;
+import org.apache.commons.lang3.tuple.Pair;
+import java.util.*;
+
+public class RegionRecallScorerV7LongTerm extends AbstractScorer4Recall {
+
+    public RegionRecallScorerV7LongTerm(ScorerConfigInfo configInfo) {
+        super(configInfo);
+    }
+    @Override
+    public void loadModel() {
+        doLoadModel(Model4RecallLongTerm.class);
+    }
+
+    @Override
+    public List<Pair<Long, Double>> recall(Map<String, String> params){
+        Model4RecallLongTerm model = (Model4RecallLongTerm) this.getModel();
+        Map<String, List<Pair<Long, Double>>> kv = model.kv;
+        Set<Pair<Long, Double>> uniqueResults = new LinkedHashSet<>();
+        for (Map.Entry<String, String> entry : params.entrySet()) {
+            String key = entry.getKey();
+            int count = Integer.parseInt(entry.getValue());
+            if (kv.containsKey(key)) {
+                List<Pair<Long, Double>> copy = new ArrayList<>(kv.get(key));
+                // 先随机，再截断。
+                List<Pair<Long, Double>> selected = getWeightedRandomElements(copy, count);
+                uniqueResults.addAll(selected);
+            }
+        }
+        return new ArrayList<>(uniqueResults); // 转换回 List 并返回
+    }
+
+    public static List<Pair<Long, Double>> getWeightedRandomElements(List<Pair<Long, Double>> list, int count) {
+        if (list == null || list.isEmpty()) {
+            return new ArrayList<>(0);
+        }
+        if (count >= list.size()) {
+            return new ArrayList<>(list);
+        }
+
+        // 计算权重总和
+        double totalWeight = list.stream().mapToDouble(Pair::getRight).sum();
+
+        // 使用加权随机选择
+        List<Pair<Long, Double>> result = new ArrayList<>();
+        Random random = new Random();
+
+        for (int i = 0; i < count; i++) {
+            double rand = random.nextDouble() * totalWeight; // 生成一个0到totalWeight之间的随机数
+            double cumulativeWeight = 0.0;
+
+            for (Pair<Long, Double> pair : list) {
+                cumulativeWeight += pair.getRight(); // 累加权重
+                if (rand <= cumulativeWeight) {
+                    result.add(pair); // 选择当前元素
+                    totalWeight -= pair.getRight(); // 更新总权重
+                    list.remove(pair); // 防止重复选择
+                    break;
+                }
+            }
+        }
+
+        return result;
+    }
+
+}

recommend-server-service/src/main/resources/feeds_recall_config_region_v7_longterm.conf

@@ -0,0 +1,7 @@
+scorer-config = {
+    score7-config = {
+        scorer-name = "com.tzld.piaoquan.recommend.server.service.score4recall.strategy.RegionRecallScorerV7LongTerm"
+        scorer-priority = 98
+        model-path = "alg_recall_file/08_vov_longterm.txt"
+    }
+}