模型实验

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/model/Video.java

@@ -29,4 +29,7 @@ public class Video {
     // video的特征 tag
     private List<String> tags = new ArrayList<>();
 
+    // video的模型打分
+    private double modelScore = 0.0D;
+
 }

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

@@ -12,7 +12,7 @@ public class RankRouter {
     public RankResult rank(RankParam param) {
         String abCode = param.getAbCode();
         switch (abCode){
-            case "70010":
+            case "60101":
                 rankService = new RankStrategy4RankModel();
                 break;
             case "60098":

recommend-server-service/src/main/java/com/tzld/piaoquan/recommend/server/service/rank/strategy/RankStrategy4RankModel.java

@@ -1,34 +1,23 @@
 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.feature.domain.video.base.ItemFeature;
-import com.tzld.piaoquan.recommend.feature.domain.video.base.RequestContext;
-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.MachineInfo;
 import com.tzld.piaoquan.recommend.server.model.Video;
-import com.tzld.piaoquan.recommend.server.remote.FeatureRemoteService;
-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.RankResult;
 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.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.apache.commons.lang3.RandomUtils;
 import org.apache.commons.lang3.math.NumberUtils;
-import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.beans.factory.annotation.Value;
+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.connection.lettuce.LettuceConnectionFactory;
 import org.springframework.data.redis.core.RedisTemplate;
 import org.springframework.stereotype.Service;
 
@@ -43,6 +32,8 @@ import java.util.stream.Collectors;
 @Slf4j
 public class RankStrategy4RankModel extends RankService {
 
+    @Value("${video.model.weight:}")
+    private Double mergeWeight;
     final private String CLASS_NAME = this.getClass().getSimpleName();
     @Override
     public List<Video> mergeAndRankRovRecall(RankParam param) {
@@ -83,11 +74,14 @@ public class RankStrategy4RankModel extends RankService {
             Collections.sort(rovRecallRank, Comparator.comparingDouble(o -> -o.getSortScore()));
         }
 
-        // todo zhangbo 增加排序str模型逻辑
-
-
-
-        return rovRecallRank;
+        //------------------- todo zhangbo 增加排序str模型逻辑 合并二者得分-------------------
+        List<Video> videosWithModel = model(rovRecallRank, param);
+        for (Video v : videosWithModel){
+            double mergeWeightIn = this.mergeWeight == null? 0.0D: this.mergeWeight;
+            v.setSortScore(v.getSortScore() + mergeWeightIn * v.getModelScore());
+        }
+        Collections.sort(videosWithModel, Comparator.comparingDouble(o -> -o.getSortScore()));
+        return videosWithModel;
     }
 
     public List<Video> model(List<Video> videos, RankParam param){
@@ -95,10 +89,20 @@ public class RankStrategy4RankModel extends RankService {
             return videos;
         }
 
+        RedisStandaloneConfiguration redisSC = new RedisStandaloneConfiguration();
+        redisSC.setPort(6379);
+        redisSC.setPassword("Wqsd@2019");
+        redisSC.setHostName("r-bp1pi8wyv6lzvgjy5z.redis.rds.aliyuncs.com");
+        RedisConnectionFactory connectionFactory = new JedisConnectionFactory(redisSC);
+        ((LettuceConnectionFactory) connectionFactory).setTimeout(1000);
+        RedisTemplate<String, String> redisTemplate = new RedisTemplate<>();
+        redisTemplate.setConnectionFactory(connectionFactory);
+        redisTemplate.afterPropertiesSet();
+
         Map<String, String> userFeatureMap = new HashMap<>();
         if (param.getMid() != null && !param.getMid().isEmpty()){
             String midKey = "user_info_4video_" + param.getMid();
-            String userFeatureStr = this.redisTemplate.opsForValue().get(midKey);
+            String userFeatureStr = redisTemplate.opsForValue().get(midKey);
             if (userFeatureStr != null){
                 try{
                     userFeatureMap = JSONUtils.fromJson(userFeatureStr,
@@ -140,7 +144,7 @@ public class RankStrategy4RankModel extends RankService {
         List<Long> videoIds = CommonCollectionUtils.toListDistinct(videos, Video::getVideoId);
         List<String> videoFeatureKeys = videoIds.stream().map(r-> "video_info_" + r)
                 .collect(Collectors.toList());
-        List<String> videoFeatures = this.redisTemplate.opsForValue().multiGet(videoFeatureKeys);
+        List<String> videoFeatures = redisTemplate.opsForValue().multiGet(videoFeatureKeys);
         if (videoFeatures != null){
             for (int i=0; i<videoFeatures.size(); ++i){
                 String vF = videoFeatures.get(i);
@@ -167,16 +171,17 @@ public class RankStrategy4RankModel extends RankService {
         }
         log.info("ItemFeature = {}", JSONUtils.toJson(videoFeatures));
 
+        ((JedisConnectionFactory) connectionFactory).destroy();
+
         Map<String, String> sceneFeatureMap =  this.getSceneFeature(param);
 
         List<RankItem> rovRecallScore = ScorerUtils.getScorerPipeline(ScorerUtils.BASE_CONF)
                 .scoring(sceneFeatureMap, userFeatureMap, rankItems);
         log.info("mergeAndRankRovRecallNew rovRecallScore={}", JSONUtils.toJson(rovRecallScore));
-
         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());
+            v.setModelScore(i.getScore());
             return v;
         });
     }

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

@@ -2,6 +2,6 @@ scorer-config = {
   related-score-config = {
     scorer-name = "com.tzld.piaoquan.recommend.server.service.score.VlogShareLRScorer"
     scorer-priority = 99
-    model-path = "model/model_new.txt"
+    model-path = "model/model_sharev2_20231220.txt"
   }
 }