feat:完善打分策略

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/score/BaseLRV2ModelScorer.java

@@ -0,0 +1,21 @@
+package com.tzld.piaoquan.ad.engine.commons.score;
+
+import com.tzld.piaoquan.ad.engine.commons.score.model.LRV2Model;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+
+public abstract class BaseLRV2ModelScorer extends AbstractScorer {
+
+    private static Logger LOGGER = LoggerFactory.getLogger(BaseLRV2ModelScorer.class);
+
+    public BaseLRV2ModelScorer(ScorerConfigInfo scorerConfigInfo) {
+        super(scorerConfigInfo);
+    }
+
+    @Override
+    public void loadModel() {
+        doLoadModel(LRV2Model.class);
+    }
+
+}

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/score/model/LRV2Model.java

@@ -0,0 +1,111 @@
+package com.tzld.piaoquan.ad.engine.commons.score.model;
+
+
+import org.apache.commons.collections4.MapUtils;
+import org.apache.commons.lang.math.NumberUtils;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStreamReader;
+import java.util.HashMap;
+import java.util.Map;
+
+
+public class LRV2Model extends Model {
+    protected static final int MODEL_FIRST_LOAD_COUNT = 1 << 25; // 32M
+    private static final Logger LOGGER = LoggerFactory.getLogger(LRV2Model.class);
+    private Map<String, Float> lrModel;
+
+    public void putFeature(Map<String, Float> model, String featureKey, float weight) {
+        model.put(featureKey, weight);
+    }
+
+    public float getWeight(Map<String, Float> model, String featureKey) {
+        return model.getOrDefault(featureKey, 0.0f);
+    }
+
+    @Override
+    public int getModelSize() {
+        if (this.lrModel == null)
+            return 0;
+        return lrModel.size();
+    }
+
+    public void cleanModel() {
+        this.lrModel = null;
+    }
+
+    public Float score(Map<String, String> featureMap) {
+        float sum = 0.0f;
+
+        if (MapUtils.isNotEmpty(featureMap)) {
+            for (Map.Entry<String, String> e : featureMap.entrySet()) {
+                float w = getWeight(this.lrModel, e.getKey());
+                sum += w * NumberUtils.toFloat(e.getValue(), 0.0f);
+            }
+
+            float biasW = lrModel.get("bias");
+            sum += biasW;
+        }
+
+
+        return (float) (1.0f / (1 + Math.exp(-sum)));
+    }
+
+    /**
+     * 目前模型比较大,分两个阶段load模型
+     * (1). load 8M 模型, 并更新;
+     * (2). load 剩余的模型
+     * 中间提供一段时间有损的打分服务
+     *
+     * @param in
+     * @return
+     * @throws IOException
+     */
+    @Override
+    public boolean loadFromStream(InputStreamReader in) throws IOException {
+
+        Map<String, Float> model = new HashMap<>();
+        BufferedReader input = new BufferedReader(in);
+        String line = null;
+        int cnt = 0;
+
+        Integer curTime = new Long(System.currentTimeMillis() / 1000).intValue();
+        //first stage
+        while ((line = input.readLine()) != null) {
+            String[] items = line.split("\t");
+            if (items.length < 2) {
+                continue;
+            }
+
+            putFeature(model, items[0], Float.valueOf(items[1].trim()).floatValue());
+            if (cnt++ < 10) {
+                LOGGER.debug("fea: " + items[0] + ", weight: " + items[1]);
+            }
+            if (cnt > MODEL_FIRST_LOAD_COUNT) {
+                break;
+            }
+        }
+        //model update
+        this.lrModel = model;
+
+        LOGGER.info("[MODELLOAD] after first stage model load, key size: {}, current time: {}", lrModel.size(), curTime);
+        //final stage
+        while ((line = input.readLine()) != null) {
+            String[] items = line.split("\t");
+            if (items.length < 2) {
+                continue;
+            }
+            putFeature(model, items[0], Float.valueOf(items[1]).floatValue());
+        }
+        LOGGER.info("[MODELLOAD] after model load, key size: {}, current time: {}", lrModel.size(), curTime);
+
+        LOGGER.info("[MODELLOAD] model load over and size " + cnt);
+        input.close();
+        in.close();
+        return true;
+    }
+
+}

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/RankService.java

@@ -14,23 +14,14 @@ public interface RankService {
 
     AdRankItem adItemRank(RankRecommendRequestParam request);
 
-    default AdRankItem adItemRankWithVideoAdThompson(RankRecommendRequestParam request) {
-        return null;
-    }
+    AdRankItem adItemRankWithVideoAdThompson(RankRecommendRequestParam request);
 
-    default AdPlatformCreativeDTO adBidRank(BidRankRecommendRequestParam request) {
-        return null;
-    }
+    AdPlatformCreativeDTO adBidRank(BidRankRecommendRequestParam request);
 
-    default AdPlatformCreativeDTO adBidRankNewPid(BidRankRecommendRequestParam request) {
-        return null;
-    }
-
-    default List<AdRankItem> rank(ScoreParam param, UserAdFeature userAdFeature, List<AdRankItem> rankItems, String configFile) {
-        return null;
-    }
+    AdPlatformCreativeDTO adBidRankNewPid(BidRankRecommendRequestParam request);
 
     default AdRankItem unionAdItemRank(UnionRankRecommendRequestParam request) {
         return null;
     }
+    List<AdRankItem> rank(ScoreParam param, UserAdFeature userAdFeature, List<AdRankItem> rankItems, String configFile);
 }

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/impl/RankService680.java → ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/RankService680.java

@@ -1,11 +1,10 @@
-package com.tzld.piaoquan.ad.engine.service.score.impl;
+package com.tzld.piaoquan.ad.engine.service.score;
 
 import com.tzld.piaoquan.ad.engine.commons.score.ScoreParam;
 import com.tzld.piaoquan.ad.engine.commons.util.ExtractorUtils;
 import com.tzld.piaoquan.ad.engine.commons.util.NumUtil;
 import com.tzld.piaoquan.ad.engine.service.feature.Feature;
 import com.tzld.piaoquan.ad.engine.service.feature.FeatureService;
-import com.tzld.piaoquan.ad.engine.service.score.RankService;
 import com.tzld.piaoquan.ad.engine.service.score.convert.RequestConvert;
 import com.tzld.piaoquan.ad.engine.service.score.dto.AdPlatformCreativeDTO;
 import com.tzld.piaoquan.ad.engine.service.score.param.RankRecommendRequestParam;
@@ -16,6 +15,10 @@ import org.apache.commons.lang3.StringUtils;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.stereotype.Service;
 
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
 import java.time.LocalDateTime;
 import java.time.ZoneOffset;
 import java.util.*;
@@ -23,13 +26,16 @@ import java.util.stream.Collectors;
 
 @Slf4j
 @Service
-public class RankService680 implements RankService {
+public class RankService680 {
 
     @Autowired
     private FeatureService featureService;
 
-    @Override
-    public AdRankItem adItemRank(RankRecommendRequestParam request) {
+    private Map<String, double[]> bucketsMap = new HashMap<>();
+
+    private Map<String, Double> bucketsLen = new HashMap<>();
+
+    public List<AdRankItem> adItemRank(RankRecommendRequestParam request) {
 
         long ts = LocalDateTime.now().toEpochSecond(ZoneOffset.of("+8")) / 1000;
 
@@ -55,7 +61,14 @@ public class RankService680 implements RankService {
 
         Map<String, String> d2Feature = videoFeature.getOrDefault("alg_cid_feature_vid_cf_rank", new HashMap<>());
 
+        List<AdRankItem> adRankItems = new ArrayList<>(request.getAdIdList().size());
         for (AdPlatformCreativeDTO dto : request.getAdIdList()) {
+            AdRankItem adRankItem = new AdRankItem();
+            adRankItem.setAdId(dto.getCreativeId());
+            adRankItem.setCreativeCode(dto.getCreativeCode());
+            adRankItem.setAdVerId(dto.getAdVerId());
+            adRankItem.setVideoId(request.getVideoId());
+
             String cidStr = dto.getCreativeId().toString();
             Map<String, String> cidFeatureMap = new HashMap<>();
             Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(cidStr, new HashMap<>());
@@ -75,8 +88,22 @@ public class RankService680 implements RankService {
 
             String title = b1Feature.getOrDefault("cidtitle", "");
             this.handleE1AndE2Feature(e1Feature, e2Feature, title, cidFeatureMap);
+
+            adRankItem.setFeatureMap(cidFeatureMap);
+
+            adRankItems.add(adRankItem);
+
         }
 
+        // 分桶
+        this.readBucketFile();
+        userFeatureMap = this.featureBucket(userFeatureMap);
+        for (AdRankItem adRankItem : adRankItems) {
+            Map<String, String> featureMap = adRankItem.getFeatureMap();
+            adRankItem.setFeatureMap(this.featureBucket(featureMap));
+        }
+
+
 
         return null;
     }
@@ -371,6 +398,57 @@ public class RankService680 implements RankService {
         return midActionStaticsMap;
     }
 
+    private void readBucketFile() {
+        InputStream resourceStream = RankService680.class.getClassLoader().getResourceAsStream("20240609_bucket_274.txt");
+        if (resourceStream != null) {
+            try (BufferedReader reader = new BufferedReader(new InputStreamReader(resourceStream))) {
+                Map<String, double[]> bucketsMap = new HashMap<>();
+                Map<String, Double> bucketsLen = new HashMap<>();
+                String line;
+                while ((line = reader.readLine()) != null) {
+                    // 替换空格和换行符，过滤空行
+                    line = line.replace(" ", "").replaceAll("\n", "");
+                    if (!line.isEmpty()) {
+                        String[] rList = line.split("\t");
+                        if (rList.length == 3) {
+                            String key = rList[0];
+                            double value1 = Double.parseDouble(rList[1]);
+                            bucketsLen.put(key, value1);
+                            double[] value2 = Arrays.stream(rList[2].split(","))
+                                    .mapToDouble(Double::valueOf)
+                                    .toArray();
+                            bucketsMap.put(key, value2);
+                        }
+                    }
+                }
+                this.bucketsMap = bucketsMap;
+                this.bucketsLen = bucketsLen;
+            } catch (IOException e) {
+                log.error("something is wrong in parse bucket file:" + e);
+            }
+        } else {
+            log.error("no bucket file");
+        }
+
+    }
+
+    private Map<String, String> featureBucket(Map<String, String> featureMap) {
+        Map<String, String> newFeatureMap = new HashMap<>(featureMap.size());
+        for (Map.Entry<String, String> entry : featureMap.entrySet()) {
+            String name = entry.getKey();
+            double score = Double.parseDouble(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.bucketsMap.containsKey(name)) {
+                double[] buckets = this.bucketsMap.get(name);
+                int bucketNum = buckets.length + 1;
+                Double scoreNew = 1.0 / bucketNum * (ExtractorUtils.findInsertPosition(buckets, score) + 1.0);
+                newFeatureMap.put(name, String.valueOf(scoreNew));
+            }
+        }
+
+        return newFeatureMap;
+    }
+
     public static class Tuple5 {
         public String f1;
         public String f2;

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/VideoAdThompsonScorerV2.java

@@ -89,7 +89,7 @@ public class VideoAdThompsonScorerV2 {
             item.setCpa(cpa);
             item.setAdId(dto.getCreativeId());
             item.setScore(score);
-            item.setFeature(ext);
+            item.setExt(ext);
             item.setVideoId(param.getVideoId());
             item.setScore_type(663);
             item.setWeight(dto.getWeight());
@@ -138,7 +138,7 @@ public class VideoAdThompsonScorerV2 {
                 ext.put("s1", s1);
                 item.setCreativeCode(dto.getCreativeCode());
                 item.setWeight(dto.getWeight());
-                item.setFeature(ext);
+                item.setExt(ext);
                 item.setScore_type(664);
             } catch (Exception e) {
                 log.error("svc=thompsonScorerByExp664 {}", gson.toJson(e.getStackTrace()));
@@ -187,7 +187,7 @@ public class VideoAdThompsonScorerV2 {
                         exp665Param.getOrDefault("alpha", 1d), exp665Param.getOrDefault("beta", 10000d), null);
                 ext.put("s1", s1);
                 item.setCreativeCode(dto.getCreativeCode());
-                item.setFeature(ext);
+                item.setExt(ext);
             } catch (Exception e) {
                 log.error("svc=thompsonScorerByExp665 {}", gson.toJson(e.getStackTrace()));
             }
@@ -255,7 +255,7 @@ public class VideoAdThompsonScorerV2 {
             item.setCpa(cpa);
             item.setAdId(dto.getCreativeId());
             item.setScore(score);
-            item.setFeature(ext);
+            item.setExt(ext);
             item.setVideoId(param.getVideoId());
             item.setScore_type(666);
             item.setWeight(dto.getWeight());
@@ -350,7 +350,7 @@ public class VideoAdThompsonScorerV2 {
             item.setAdId(dto.getCreativeId());
             item.setWeight(dto.getWeight());
             item.setScore(score);
-            item.setFeature(ext);
+            item.setExt(ext);
             item.setBid1(dto.getBid1());
             item.setBid2(dto.getBid2());
             item.setCreativeCode(dto.getCreativeCode());
@@ -413,7 +413,7 @@ public class VideoAdThompsonScorerV2 {
             item.setAdId(dto.getCreativeId());
             item.setWeight(dto.getWeight());
             item.setScore(score);
-            item.setFeature(ext);
+            item.setExt(ext);
             item.setBid1(dto.getBid1());
             item.setBid2(dto.getBid2());
             item.setCreativeCode(dto.getCreativeCode());
@@ -567,7 +567,7 @@ public class VideoAdThompsonScorerV2 {
             item.setCpa(cpa);
             item.setAdId(dto.getCreativeId());
             item.setScore(score);
-            item.setFeature(ext);
+            item.setExt(ext);
             item.setVideoId(param.getVideoId());
             item.setScore_type(scoreType);
             item.setWeight(dto.getWeight());

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/VlogRovLRScorer.java

@@ -0,0 +1,153 @@
+package com.tzld.piaoquan.ad.engine.service.score;
+
+
+import com.tzld.piaoquan.ad.engine.commons.score.BaseLRV2ModelScorer;
+import com.tzld.piaoquan.ad.engine.commons.score.ScoreParam;
+import com.tzld.piaoquan.ad.engine.commons.score.ScorerConfigInfo;
+import com.tzld.piaoquan.ad.engine.commons.score.model.LRV2Model;
+import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdRankItem;
+import com.tzld.piaoquan.recommend.feature.domain.ad.base.UserAdFeature;
+import org.apache.commons.collections4.CollectionUtils;
+import org.apache.commons.collections4.MapUtils;
+import org.apache.commons.lang.exception.ExceptionUtils;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.*;
+import java.util.concurrent.*;
+
+
+public class VlogRovLRScorer extends BaseLRV2ModelScorer {
+
+    private static final int LOCAL_TIME_OUT = 150;
+    private final static Logger LOGGER = LoggerFactory.getLogger(VlogRovLRScorer.class);
+    private static final ExecutorService executorService = Executors.newFixedThreadPool(128);
+
+
+    public VlogRovLRScorer(ScorerConfigInfo configInfo) {
+        super(configInfo);
+    }
+
+    @Override
+    public List<AdRankItem> scoring(final ScoreParam param,
+                                    final UserAdFeature userFeature,
+                                    final List<AdRankItem> rankItems) {
+        throw new NoSuchMethodError();
+    }
+
+    public List<AdRankItem> scoring(final Map<String, String> sceneFeatureMap,
+                                    final Map<String, String> userFeatureMap,
+                                    final List<AdRankItem> rankItems) {
+        if (CollectionUtils.isEmpty(rankItems)) {
+            return rankItems;
+        }
+
+        long startTime = System.currentTimeMillis();
+        LRV2Model model = (LRV2Model) this.getModel();
+        LOGGER.debug("model size: [{}]", model.getModelSize());
+
+        List<AdRankItem> result = rankByJava(sceneFeatureMap, userFeatureMap, rankItems);
+
+        LOGGER.debug("ctr ranker time java items size={}, time={} ", result.size(),
+                System.currentTimeMillis() - startTime);
+
+        return result;
+    }
+
+    private List<AdRankItem> rankByJava(final Map<String, String> sceneFeatureMap,
+                                        final Map<String, String> userFeatureMap,
+                                        final List<AdRankItem> items) {
+        long startTime = System.currentTimeMillis();
+        LRV2Model model = (LRV2Model) this.getModel();
+        // 所有都参与打分，按照ctr排序
+        multipleCtrScore(items, userFeatureMap, sceneFeatureMap, model);
+
+        // debug log
+        if (LOGGER.isDebugEnabled()) {
+            for (AdRankItem item : items) {
+                LOGGER.debug("before enter feeds model predict ctr score [{}] [{}]", item, item);
+            }
+        }
+
+        Collections.sort(items);
+
+        LOGGER.debug("[ctr ranker time java] items size={}, cost={} ", items.size(),
+                System.currentTimeMillis() - startTime);
+        return items;
+    }
+
+    private void multipleCtrScore(final List<AdRankItem> items,
+                                  final Map<String, String> userFeatureMap,
+                                  final Map<String, String> sceneFeatureMap,
+                                  final LRV2Model model) {
+
+        List<Callable<Object>> calls = new ArrayList<Callable<Object>>();
+        for (int index = 0; index < items.size(); index++) {
+            final int fIndex = index;
+            calls.add(new Callable<Object>() {
+                @Override
+                public Object call() throws Exception {
+                    try {
+                        calcScore(model, items.get(fIndex), userFeatureMap, sceneFeatureMap);
+                    } catch (Exception e) {
+                        LOGGER.error("ctr exception: [{}] [{}]", items.get(fIndex), ExceptionUtils.getFullStackTrace(e));
+                    }
+                    return new Object();
+                }
+            });
+        }
+
+        List<Future<Object>> futures = null;
+        try {
+            futures = executorService.invokeAll(calls, LOCAL_TIME_OUT, TimeUnit.MILLISECONDS);
+        } catch (InterruptedException e) {
+            LOGGER.error("execute invoke fail: {}", ExceptionUtils.getFullStackTrace(e));
+        }
+
+        // 等待所有请求的结果返回, 超时也返回
+        int cancel = 0;
+        if (futures != null) {
+            for (Future<Object> future : futures) {
+                try {
+                    if (!future.isDone() || future.isCancelled() || future.get() == null) {
+                        cancel++;
+                    }
+                } catch (InterruptedException e) {
+                    LOGGER.error("InterruptedException: ", e);
+                } catch (ExecutionException e) {
+                    LOGGER.error("ExecutionException {},{}", sceneFeatureMap.size(),
+                            ExceptionUtils.getFullStackTrace(e));
+                }
+            }
+        }
+    }
+
+    public double calcScore(final LRV2Model lrModel,
+                            final AdRankItem item,
+                            final Map<String, String> userFeatureMap,
+                            final Map<String, String> sceneFeatureMap) {
+
+
+        Map<String, String> featureMap = new HashMap<>();
+        if (MapUtils.isNotEmpty(item.getFeatureMap())) {
+            featureMap.putAll(item.getFeatureMap());
+        }
+        if (MapUtils.isNotEmpty(userFeatureMap)) {
+            featureMap.putAll(userFeatureMap);
+        }
+        if (MapUtils.isNotEmpty(sceneFeatureMap)) {
+            featureMap.putAll(sceneFeatureMap);
+        }
+
+        double pro = 0.0;
+        if (MapUtils.isNotEmpty(featureMap)) {
+            try {
+                pro = lrModel.score(featureMap);
+                // LOGGER.info("fea : {}, score:{}", JSONUtils.toJson(featureMap), pro);
+            } catch (Exception e) {
+                LOGGER.error("score error for doc={} exception={}", item.getVideoId(), ExceptionUtils.getFullStackTrace(e));
+            }
+        }
+        return pro;
+    }
+}

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/dto/AdDirectionScore.java

@@ -0,0 +1,17 @@
+package com.tzld.piaoquan.ad.engine.service.score.dto;
+
+import lombok.Builder;
+import lombok.Data;
+
+import java.util.concurrent.ConcurrentHashMap;
+
+@Data
+@Builder
+public class AdDirectionScore {
+
+    private Double exponent;
+
+
+    private ConcurrentHashMap<String, Double> scoreDetail;
+
+}

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/dto/AdPlatformCreativeDTO.java

@@ -37,7 +37,7 @@ public class AdPlatformCreativeDTO {
     /**
      * 定向打分参数
      */
-    private HumanDto humanScore;
+    private AdDirectionScore adDirectionScore;
 
     public static void main(String[] args) {
         System.out.println(JSON.toJSONString(AdPlatformCreativeDTO.builder()

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/dto/HumanDto.java

@@ -1,18 +0,0 @@
-package com.tzld.piaoquan.ad.engine.service.score.dto;
-
-import lombok.AllArgsConstructor;
-import lombok.Data;
-import lombok.NoArgsConstructor;
-
-import java.util.Map;
-
-@Data
-@NoArgsConstructor
-@AllArgsConstructor
-public class HumanDto {
-
-    private Double exponent;
-
-    private Map<String, Double> detail;
-
-}

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/impl/RankServiceImpl.java

@@ -10,6 +10,7 @@ import com.tzld.piaoquan.ad.engine.service.predict.helper.NewExpInfoHelper;
 import com.tzld.piaoquan.ad.engine.service.predict.param.ThresholdPredictModelParam;
 import com.tzld.piaoquan.ad.engine.service.remote.FeatureRemoteService;
 import com.tzld.piaoquan.ad.engine.service.score.RankService;
+import com.tzld.piaoquan.ad.engine.service.score.RankService680;
 import com.tzld.piaoquan.ad.engine.service.score.VideoAdThompsonScorerV2;
 import com.tzld.piaoquan.ad.engine.service.score.container.AdCreativeFeatureContainer;
 import com.tzld.piaoquan.ad.engine.service.score.container.PidLambdaContainer;
@@ -81,9 +82,9 @@ public class RankServiceImpl implements RankService {
         Long appType = requestParam.getAppType();
         Integer newExpGroup = requestParam.getNewExpGroup();
         if (AbUtil.isInAbExp(expCodeSet, appType, newExpGroup, fmModelScoreExpCode)) {
-            return fmRankService.adItemRank(requestParam);
+            return fmRankService.adItemRank(requestParam).get(0);
         }else if (AbUtil.isInAbExp(expCodeSet, appType, newExpGroup, tacticsAndFmModelScoreExpCode)){
-            return tacticsAndFmModelScoreRankService.adItemRank(requestParam);
+            // return tacticsAndFmModelScoreRankService.adItemRank(requestParam).get;
         }
         return adItemRankOld(requestParam);
     }
@@ -208,7 +209,7 @@ public class RankServiceImpl implements RankService {
             }
 
             for (AdRankItem adRankItem : rankResult) {
-                adRankItem.getFeature().put("userAdFeature", JSON.toJSONString(userAdFeature));
+                adRankItem.getExt().put("userAdFeature", JSON.toJSONString(userAdFeature));
             }
             // 日志上报
             logHubService.scoreLogUpload(param, request.getAdIdList(), rankResult, request,

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/impl/TacticsAndFmModelScoreRankService.java

@@ -1,16 +1,9 @@
 package com.tzld.piaoquan.ad.engine.service.score.impl;
 
-import com.tzld.piaoquan.ad.engine.service.score.RankService;
-import com.tzld.piaoquan.ad.engine.service.score.param.RankRecommendRequestParam;
-import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdRankItem;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.stereotype.Service;
 
 @Slf4j
 @Service
-public class TacticsAndFmModelScoreRankService implements RankService {
-    @Override
-    public AdRankItem adItemRank(RankRecommendRequestParam request) {
-        return null;
-    }
+public class TacticsAndFmModelScoreRankService {
 }