feat:v2模型

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

@@ -32,7 +32,9 @@ public final class ScorerUtils {
     public static String LR_ROV_SCORE_20240626 = "ad_score_config_20240626.conf";
     public static String LR_ROV_SCORE_20240813 = "ad_score_config_20240813.conf";
 
-    public static String  XGBOOST_SCORE_CONF = "ad_score_config_xgboost.conf";
+    public static String XGBOOST_SCORE_CONF = "ad_score_config_xgboost.conf";
+    public static String XGBOOST_SCORE_CONF_683 = "ad_score_config_xgboost_683.conf";
+
     public static void warmUp() {
         log.info("scorer warm up ");
         // ScorerUtils.init(BASE_CONF);
@@ -43,8 +45,9 @@ public final class ScorerUtils {
         // ScorerUtils.init(VIDEO_CREATIVE_THOMPSON);
 
         ScorerUtils.init(LR_ROV_SCORE_20240626);
-        ScorerUtils.init(LR_ROV_SCORE_20240813);
+        // ScorerUtils.init(LR_ROV_SCORE_20240813);
 
+        ScorerUtils.init(XGBOOST_SCORE_CONF_683);
         ScorerUtils.init(XGBOOST_SCORE_CONF);
     }
 

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/score/model/XGBoost351Model.java → ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/score/model/XGBoostModel683.java

@@ -15,8 +15,8 @@ import java.io.InputStreamReader;
 import java.util.Map;
 
 
-public class XGBoost351Model extends Model {
-    private static final Logger LOGGER = LoggerFactory.getLogger(XGBoost351Model.class);
+public class XGBoostModel683 extends Model {
+    private static final Logger LOGGER = LoggerFactory.getLogger(XGBoostModel683.class);
     private XGBoostClassificationModel model;
 
     private String[] features = {

ad-engine-commons/src/main/java/com/tzld/piaoquan/ad/engine/commons/util/ComparatorUtil.java

@@ -0,0 +1,23 @@
+package com.tzld.piaoquan.ad.engine.commons.util;
+
+import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdRankItem;
+
+import java.util.Comparator;
+import java.util.Random;
+
+public class ComparatorUtil {
+
+    public static Comparator<AdRankItem> equalsRandomComparator() {
+        return new Comparator<AdRankItem>() {
+            @Override
+            public int compare(AdRankItem o1, AdRankItem o2) {
+                int comparison = o1.compareTo(o2);
+                if (comparison != 0) {
+                    return comparison;
+                }
+                // 数字相等时随机排列
+                return new Random().nextInt(3) - 1; // 产生-1、0或1的随机值
+            }
+        };
+    }
+}

ad-engine-server/src/main/resources/ad_score_config_xgboost_683.conf

@@ -0,0 +1,8 @@
+scorer-config = {
+  lr-rov-score-config = {
+    scorer-name = "com.tzld.piaoquan.ad.engine.service.score.XGBoostScorer683"
+    scorer-priority = 99
+    model-path = "zhangbo/model_xgb_351_1000.tar.gz"
+  }
+
+}

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

@@ -3,10 +3,7 @@ package com.tzld.piaoquan.ad.engine.service.score;
 import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
 import com.tzld.piaoquan.ad.engine.commons.score.ScoreParam;
 import com.tzld.piaoquan.ad.engine.commons.score.ScorerUtils;
-import com.tzld.piaoquan.ad.engine.commons.util.DateUtils;
-import com.tzld.piaoquan.ad.engine.commons.util.ExtractorUtils;
-import com.tzld.piaoquan.ad.engine.commons.util.NumUtil;
-import com.tzld.piaoquan.ad.engine.commons.util.ObjUtil;
+import com.tzld.piaoquan.ad.engine.commons.util.*;
 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.dto.AdPlatformCreativeDTO;
@@ -45,7 +42,6 @@ public class RankService680 {
 
         long ts = System.currentTimeMillis() / 1000;
 
-
         // 特征处理
         Feature feature = this.getFeature(scoreParam, request);
 
@@ -165,7 +161,7 @@ public class RankService680 {
             }
         }
 
-        Collections.sort(result);
+        result.sort(ComparatorUtil.equalsRandomComparator());
 
         return result;
     }

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/RankService683.java

@@ -0,0 +1,589 @@
+package com.tzld.piaoquan.ad.engine.service.score;
+
+import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
+import com.tzld.piaoquan.ad.engine.commons.score.ScoreParam;
+import com.tzld.piaoquan.ad.engine.commons.score.ScorerUtils;
+import com.tzld.piaoquan.ad.engine.commons.util.*;
+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.dto.AdPlatformCreativeDTO;
+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.apache.commons.collections4.MapUtils;
+import org.apache.commons.lang3.StringUtils;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.stereotype.Service;
+import org.xm.Similarity;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.util.*;
+import java.util.stream.Collectors;
+
+@Slf4j
+@Service
+public class RankService683 {
+
+    @ApolloJsonValue("${no.postback.conversion.adverids:[]}")
+    private Set<String> noPostbackConversionAdVerIds;
+
+    @Autowired
+    private FeatureService featureService;
+
+    private Map<String, double[]> bucketsMap = new HashMap<>();
+
+    private Map<String, Double> bucketsLen = new HashMap<>();
+
+    public List<AdRankItem> adItemRank(RankRecommendRequestParam request, ScoreParam scoreParam) {
+
+        long ts = System.currentTimeMillis() / 1000;
+
+        // 特征处理
+        Feature feature = this.getFeature(scoreParam, request);
+
+        Map<String, Map<String, String>> userFeature = feature.getUserFeature();
+        Map<String, Map<String, String>> videoFeature = feature.getVideoFeature();
+        Map<String, Map<String, Map<String, String>>> allAdVerFeature = feature.getAdVerFeature();
+        Map<String, Map<String, Map<String, String>>> allCidFeature = feature.getCidFeature();
+
+        Map<String, String> userFeatureMap = new HashMap<>();
+        Map<String, String> c1Feature = userFeature.getOrDefault("alg_mid_feature_ad_action", new HashMap<>());
+        List<TupleMapEntry<Tuple5>> midActionList = this.handleC1Feature(c1Feature, userFeatureMap);
+
+        Map<String, Double> midTimeDiffMap = this.parseC1FeatureListToTimeDiffMap(midActionList, ts);
+        Map<String, Double> actionStaticMap = this.parseC1FeatureListToActionStaticMap(midActionList);
+
+        Map<String, String> d2Feature = videoFeature.getOrDefault("alg_cid_feature_vid_cf_rank", new HashMap<>());
+        Map<String, String> d3Feature = videoFeature.getOrDefault("alg_vid_feature_basic_info", new HashMap<>());
+
+        Map<String, Map<String, Double>> vidRankMaps = this.parseD2FeatureMap(d2Feature);
+
+        Map<String, String> e1Feature = userFeature.getOrDefault("alg_mid_feature_return_tags", new HashMap<>());
+        Map<String, String> e2Feature = userFeature.getOrDefault("alg_mid_feature_share_tags", new HashMap<>());
+
+        Map<String, String> sceneFeatureMap = this.handleSceneFeature(ts);
+
+        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());
+            adRankItem.setCpa(dto.getCpa());
+            adRankItem.setId(dto.getAdId());
+            adRankItem.setCampaignId(dto.getCampaignId());
+            adRankItem.setCpm(ObjUtil.nullOrDefault(dto.getCpm(), 90).doubleValue());
+
+            String cidStr = dto.getCreativeId().toString();
+            Map<String, String> cidFeatureMap = new HashMap<>();
+            Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(cidStr, new HashMap<>());
+            Map<String, String> b1Feature = cidFeature.getOrDefault("alg_cid_feature_basic_info", new HashMap<>());
+
+            Map<String, Map<String, String>> adVerFeature = allAdVerFeature.getOrDefault(dto.getAdVerId(), new HashMap<>());
+
+            Map<String, String> d1Feature = cidFeature.getOrDefault("alg_cid_feature_vid_cf", new HashMap<>());
+
+            this.handleB1Feature(b1Feature, cidFeatureMap, cidStr);
+
+            this.handleB2ToB5AndB8ToB9Feature(cidFeature, adVerFeature, cidFeatureMap);
+
+            this.handleB6ToB7Feature(cidFeature, cidFeatureMap);
+
+            this.handleC1UIFeature(midTimeDiffMap, actionStaticMap, cidFeatureMap, cidStr);
+
+            this.handleD1Feature(d1Feature, cidFeatureMap);
+
+            this.handleD2Feature(vidRankMaps, cidFeatureMap, cidStr);
+
+            String title = b1Feature.getOrDefault("cidtitle", "");
+            this.handleE1AndE2Feature(e1Feature, e2Feature, title, cidFeatureMap);
+
+            this.handleD3AndB1Feature(d3Feature, 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));
+        }
+
+        // 打分排序
+        List<AdRankItem> result = ScorerUtils.getScorerPipeline(ScorerUtils.XGBOOST_SCORE_CONF_683)
+                .scoring(sceneFeatureMap, userFeatureMap, adRankItems);
+        for (AdRankItem item : result) {
+            item.setScore(item.getLrScore() * item.getCpa());
+            item.getScoreMap().put("cpa", item.getCpa());
+            item.getScoreMap().put("cpm", item.getCpm());
+            item.getFeatureMap().putAll(userFeatureMap);
+            item.getFeatureMap().putAll(sceneFeatureMap);
+
+            // 没有转化回传的广告主，使用后台配置的CPM
+            if (noPostbackConversionAdVerIds.contains(item.getAdVerId())) {
+                item.setScore(item.getCpm() / 1000);
+            }
+
+            for (Map.Entry<String, Map<String, String>> entry : videoFeature.entrySet()) {
+                if (MapUtils.isNotEmpty(entry.getValue())) {
+                    item.getMetaFeatureMap().put(entry.getKey(), entry.getValue());
+                }
+            }
+
+            for (Map.Entry<String, Map<String, String>> entry : userFeature.entrySet()) {
+                if (MapUtils.isNotEmpty(entry.getValue())) {
+                    item.getMetaFeatureMap().put(entry.getKey(), entry.getValue());
+                }
+            }
+
+            Map<String, Map<String, String>> adVerFeature = allAdVerFeature.getOrDefault(item.getAdVerId(), new HashMap<>());
+            for (Map.Entry<String, Map<String, String>> entry : adVerFeature.entrySet()) {
+                if (MapUtils.isNotEmpty(entry.getValue())) {
+                    item.getMetaFeatureMap().put(entry.getKey(), entry.getValue());
+                }
+            }
+
+            Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(String.valueOf(item.getAdId()), new HashMap<>());
+            for (Map.Entry<String, Map<String, String>> entry : cidFeature.entrySet()) {
+                if (MapUtils.isNotEmpty(entry.getValue())) {
+                    item.getMetaFeatureMap().put(entry.getKey(), entry.getValue());
+                }
+            }
+        }
+
+        result.sort(ComparatorUtil.equalsRandomComparator());
+
+        return result;
+    }
+
+    private Feature getFeature(ScoreParam param, RankRecommendRequestParam request) {
+        List<AdPlatformCreativeDTO> adIdList = request.getAdIdList();
+        List<String> cidList = adIdList.stream()
+                .map(AdPlatformCreativeDTO::getCreativeId)
+                .map(Object::toString)
+                .collect(Collectors.toList());
+
+        List<String> adVerIdList = adIdList.stream()
+                .map(AdPlatformCreativeDTO::getAdVerId)
+                .filter(StringUtils::isNotBlank)
+                .distinct()
+                .collect(Collectors.toList());
+        return featureService.getFeature(cidList, adVerIdList, param);
+    }
+
+    private void handleB1Feature(Map<String, String> b1Feature, Map<String, String> cidFeatureMap, String cid) {
+        cidFeatureMap.put("cid_" + cid, "0.1");
+        // if (StringUtils.isNotBlank(b1Feature.get("adid"))) {
+        //     String adId = b1Feature.get("adid");
+        //     cidFeatureMap.put("adid_" + adId, idDefaultValue);
+        // }
+        if (StringUtils.isNotBlank(b1Feature.get("adverid"))) {
+            String adVerId = b1Feature.get("adverid");
+            cidFeatureMap.put("adverid_" + adVerId, "0.1");
+        }
+        // if (StringUtils.isNotBlank(b1Feature.get("targeting_conversion"))) {
+        //     String targetingConversion = b1Feature.get("targeting_conversion");
+        //     cidFeatureMap.put("targeting_conversion_" + targetingConversion, idDefaultValue);
+        // }
+        if (StringUtils.isNotBlank(b1Feature.get("cpa"))) {
+            String cpa = b1Feature.get("cpa");
+            cidFeatureMap.put("cpa", cpa);
+        }
+    }
+
+    private void handleB2ToB5AndB8ToB9Feature(Map<String, Map<String, String>> c1Feature, Map<String, Map<String, String>> adVerFeature, Map<String, String> cidFeatureMap) {
+        Map<String, String> b2Feature = adVerFeature.getOrDefault("alg_cid_feature_adver_action", new HashMap<>());
+        Map<String, String> b3Feature = c1Feature.getOrDefault("alg_cid_feature_cid_action", new HashMap<>());
+        Map<String, String> b4Feature = c1Feature.getOrDefault("alg_cid_feature_region_action", new HashMap<>());
+        Map<String, String> b5Feature = c1Feature.getOrDefault("alg_cid_feature_app_action", new HashMap<>());
+        Map<String, String> b8Feature = c1Feature.getOrDefault("alg_cid_feature_brand_action", new HashMap<>());
+        Map<String, String> b9Feature = c1Feature.getOrDefault("alg_cid_feature_weChatVersion_action", new HashMap<>());
+
+        List<String> timeList = Arrays.asList("1h", "2h", "3h", "6h", "12h", "1d", "3d", "7d", "yesterday", "today");
+        List<Tuple2<Map<String, String>, String>> featureList = Arrays.asList(
+                new Tuple2<>(b2Feature, "b2"),
+                new Tuple2<>(b3Feature, "b3"),
+                new Tuple2<>(b4Feature, "b4"),
+                new Tuple2<>(b5Feature, "b5"),
+                new Tuple2<>(b8Feature, "b8"),
+                new Tuple2<>(b9Feature, "b9")
+        );
+        for (Tuple2<Map<String, String>, String> tuple2 : featureList) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            for (String time : timeList) {
+                double view = Double.parseDouble(feature.getOrDefault("ad_view_" + time, "0"));
+                double click = Double.parseDouble(feature.getOrDefault("ad_click_" + time, "0"));
+                double conver = Double.parseDouble(feature.getOrDefault("ad_conversion_" + time, "0"));
+                double income = Double.parseDouble(feature.getOrDefault("ad_income_" + time, "0"));
+                double f2 = NumUtil.div(conver, view);
+                cidFeatureMap.put(prefix + "_" + time + "_ctr", String.valueOf(NumUtil.div(click, view)));
+                cidFeatureMap.put(prefix + "_" + time + "_ctcvr", String.valueOf(f2));
+                cidFeatureMap.put(prefix + "_" + time + "_cvr", String.valueOf(NumUtil.div(conver, click)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver", String.valueOf(conver));
+                // cidFeatureMap.put(prefix + "_" + time + "_ecpm", String.valueOf(NumUtil.div(income * 1000, view)));
+
+                cidFeatureMap.put(prefix + "_" + time + "_click", String.valueOf(click));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*log(view)", String.valueOf(conver * NumUtil.log(view)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*ctcvr", String.valueOf(conver * f2));
+            }
+        }
+
+    }
+
+    private void handleB6ToB7Feature(Map<String, Map<String, String>> c1Feature, Map<String, String> cidFeatureMap) {
+        Map<String, String> b6Feature = c1Feature.getOrDefault("alg_cid_feature_week_action", new HashMap<>());
+        Map<String, String> b7Feature = c1Feature.getOrDefault("alg_cid_feature_hour_action", new HashMap<>());
+
+        List<String> timeList = Arrays.asList("7d", "14d");
+        List<Tuple2<Map<String, String>, String>> featureList = Arrays.asList(
+                new Tuple2<>(b6Feature, "b6"),
+                new Tuple2<>(b7Feature, "b7")
+        );
+        for (Tuple2<Map<String, String>, String> tuple2 : featureList) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            for (String time : timeList) {
+                double view = Double.parseDouble(feature.getOrDefault("ad_view_" + time, "0"));
+                double click = Double.parseDouble(feature.getOrDefault("ad_click_" + time, "0"));
+                double conver = Double.parseDouble(feature.getOrDefault("ad_conversion_" + time, "0"));
+                double income = Double.parseDouble(feature.getOrDefault("ad_income_" + time, "0"));
+                double f2 = NumUtil.div(conver, view);
+                cidFeatureMap.put(prefix + "_" + time + "_ctr", String.valueOf(NumUtil.div(click, view)));
+                cidFeatureMap.put(prefix + "_" + time + "_ctcvr", String.valueOf(f2));
+                cidFeatureMap.put(prefix + "_" + time + "_cvr", String.valueOf(NumUtil.div(conver, click)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver", String.valueOf(conver));
+                // cidFeatureMap.put(prefix + "_" + time + "_ecpm", String.valueOf(NumUtil.div(income * 1000, view)));
+
+                cidFeatureMap.put(prefix + "_" + time + "_click", String.valueOf(click));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*log(view)", String.valueOf(conver * NumUtil.log(view)));
+                cidFeatureMap.put(prefix + "_" + time + "_conver*ctcvr", String.valueOf(conver * f2));
+            }
+        }
+
+    }
+
+    private List<TupleMapEntry<Tuple5>> handleC1Feature(Map<String, String> c1Feature, Map<String, String> featureMap) {
+
+        // 用户特征
+        List<TupleMapEntry<Tuple5>> midActionList = new ArrayList<>();
+        if (c1Feature.containsKey("action")) {
+            String action = c1Feature.get("action");
+            midActionList = Arrays.stream(action.split(","))
+                    .map(r -> {
+                        String[] rList = r.split(":");
+                        Tuple5 tuple5 = new Tuple5(rList[1], rList[2], rList[3], rList[4], rList[5]);
+                        return new TupleMapEntry<>(rList[0], tuple5);
+                    })
+                    // TODO 倒排
+                    .sorted((a, b) -> Integer.compare(Integer.parseInt(b.value.f1), Integer.parseInt(a.value.f1)))
+                    .collect(Collectors.toList());
+        }
+
+        double viewAll = midActionList.size();
+        double clickAll = midActionList.stream().mapToInt(e -> Integer.parseInt(e.value.f2)).sum();
+        double converAll = midActionList.stream().mapToInt(e -> Integer.parseInt(e.value.f3)).sum();
+        double incomeAll = midActionList.stream().mapToInt(e -> Integer.parseInt(e.value.f4)).sum();
+        featureMap.put("viewAll", String.valueOf(viewAll));
+        featureMap.put("clickAll", String.valueOf(clickAll));
+        featureMap.put("converAll", String.valueOf(converAll));
+        featureMap.put("incomeAll", String.valueOf(incomeAll));
+        featureMap.put("ctr_all", String.valueOf(NumUtil.div(clickAll, viewAll)));
+        featureMap.put("ctcvr_all", String.valueOf(NumUtil.div(converAll, viewAll)));
+        featureMap.put("cvr_all", String.valueOf(NumUtil.div(clickAll, converAll)));
+        // featureMap.put("ecpm_all", String.valueOf(NumUtil.div(incomeAll * 1000, viewAll)));
+
+        return midActionList;
+    }
+
+    private void handleC1UIFeature(Map<String, Double> midTimeDiffMap, Map<String, Double> midActionStatic, Map<String, String> featureMap, String cid) {
+        if (midTimeDiffMap.containsKey("timediff_view_" + cid)) {
+            featureMap.put("timediff_view", String.valueOf(midTimeDiffMap.getOrDefault("timediff_view_" + cid, 0.0)));
+        }
+        if (midTimeDiffMap.containsKey("timediff_click_" + cid)) {
+            featureMap.put("timediff_click", String.valueOf(midTimeDiffMap.getOrDefault("timediff_click_" + cid, 0.0)));
+        }
+        if (midTimeDiffMap.containsKey("timediff_conver_" + cid)) {
+            featureMap.put("timediff_conver", String.valueOf(midTimeDiffMap.getOrDefault("timediff_conver_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_view_" + cid)) {
+            featureMap.put("actionstatic_view", String.valueOf(midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_click_" + cid)) {
+            featureMap.put("actionstatic_click", String.valueOf(midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_conver_" + cid)) {
+            featureMap.put("actionstatic_conver", String.valueOf(midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_income_" + cid)) {
+            featureMap.put("actionstatic_income", String.valueOf(midActionStatic.getOrDefault("actionstatic_income_" + cid, 0.0)));
+        }
+        if (midActionStatic.containsKey("actionstatic_view_" + cid) && midActionStatic.containsKey("actionstatic_click_" + cid)) {
+            double ctr = NumUtil.div(
+                    midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0),
+                    midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)
+            );
+            featureMap.put("actionstatic_ctr", String.valueOf(ctr));
+        }
+        if (midActionStatic.containsKey("actionstatic_view_" + cid) && midActionStatic.containsKey("actionstatic_conver_" + cid)) {
+            double ctcvr = NumUtil.div(
+                    midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0),
+                    midActionStatic.getOrDefault("actionstatic_view_" + cid, 0.0)
+            );
+            featureMap.put("actionstatic_ctcvr", String.valueOf(ctcvr));
+        }
+        if (midActionStatic.containsKey("actionstatic_conver_" + cid) && midActionStatic.containsKey("actionstatic_click_" + cid)) {
+            double cvr = NumUtil.div(
+                    midActionStatic.getOrDefault("actionstatic_conver_" + cid, 0.0),
+                    midActionStatic.getOrDefault("actionstatic_click_" + cid, 0.0)
+            );
+            featureMap.put("actionstatic_cvr", String.valueOf(cvr));
+        }
+    }
+
+    private void handleD1Feature(Map<String, String> d1Feature, Map<String, String> featureMap) {
+        for (String prefix : Arrays.asList("3h", "6h", "12h", "1d", "3d", "7d")) {
+            double view = Double.parseDouble(d1Feature.getOrDefault("ad_view_" + prefix, "0"));
+            double click = Double.parseDouble(d1Feature.getOrDefault("ad_click_" + prefix, "0"));
+            double conver = Double.parseDouble(d1Feature.getOrDefault("ad_conversion_" + prefix, "0"));
+            double income = Double.parseDouble(d1Feature.getOrDefault("ad_income_" + prefix, "0"));
+            featureMap.put("d1_feature_" + prefix + "_ctr", String.valueOf(NumUtil.div(click, view)));
+            featureMap.put("d1_feature_" + prefix + "_ctcvr", String.valueOf(NumUtil.div(conver, view)));
+            featureMap.put("d1_feature_" + prefix + "_cvr", String.valueOf(NumUtil.div(conver, click)));
+            featureMap.put("d1_feature_" + prefix + "_conver", String.valueOf(conver));
+            // featureMap.put("d1_feature_" + prefix + "_ecpm", String.valueOf(NumUtil.div(income * 1000, view)));
+        }
+    }
+
+    private void handleD2Feature(Map<String, Map<String, Double>> vidRankMaps, Map<String, String> featureMap, String cid) {
+        if (MapUtils.isEmpty(vidRankMaps)) {
+            return;
+        }
+
+        // List<String> prefixes1 = Arrays.asList("ctr", "ctcvr", "ecpm");
+        List<String> prefixes1 = Arrays.asList("ctr", "ctcvr");
+        List<String> prefixes2 = Arrays.asList("1d", "3d", "7d", "14d");
+
+        for (String prefix1 : prefixes1) {
+            for (String prefix2 : prefixes2) {
+                String combinedKey = prefix1 + "_" + prefix2;
+                if (vidRankMaps.containsKey(combinedKey)) {
+                    Double rank = vidRankMaps.get(combinedKey).getOrDefault(cid, 0.0);
+                    if (rank >= 1.0) {
+                        featureMap.put("vid_rank_" + combinedKey, String.valueOf(NumUtil.div(1, rank)));
+                    }
+                }
+            }
+        }
+    }
+
+    private void handleD3AndB1Feature(Map<String, String> d3Feature, String cTitle, Map<String, String> featureMap) {
+        if (MapUtils.isEmpty(d3Feature) || !d3Feature.containsKey("title") || StringUtils.isEmpty(cTitle)) {
+            return;
+        }
+        String vTitle = d3Feature.get("title");
+        double score = Similarity.conceptSimilarity(cTitle, vTitle);
+        featureMap.put("ctitle_vtitle_similarity", String.valueOf(score));
+    }
+
+    private void handleE1AndE2Feature(Map<String, String> e1Feature, Map<String, String> e2Feature, String title, Map<String, String> featureMap) {
+        if (StringUtils.isEmpty(title)) {
+            return;
+        }
+
+        List<Tuple2<Map<String, String>, String>> tuple2List = Arrays.asList(
+                new Tuple2<>(e1Feature, "e1"),
+                new Tuple2<>(e2Feature, "e2")
+        );
+
+        List<String> tagsFieldList = Arrays.asList("tags_3d", "tags_7d", "tags_14d");
+        for (Tuple2<Map<String, String>, String> tuple2 : tuple2List) {
+            Map<String, String> feature = tuple2.f1;
+            String prefix = tuple2.f2;
+            if (MapUtils.isEmpty(feature)) {
+                continue;
+            }
+
+            for (String tagsField : tagsFieldList) {
+                if (StringUtils.isNotEmpty(feature.get(tagsField))) {
+                    String tags = feature.get(tagsField);
+                    Double[] doubles = ExtractorUtils.funcC34567ForTags(tags, title);
+                    featureMap.put(prefix + "_" + tagsField + "_matchnum", String.valueOf(doubles[0]));
+                    featureMap.put(prefix + "_" + tagsField + "_maxscore", String.valueOf(doubles[1]));
+                    featureMap.put(prefix + "_" + tagsField + "_avgscore", String.valueOf(doubles[2]));
+                }
+            }
+        }
+    }
+
+    private Map<String, Double> parseC1FeatureListToTimeDiffMap(List<TupleMapEntry<Tuple5>> midActionList, long ts) {
+        Map<String, Double> midTimeDiffMap = new HashMap<>();
+        for (TupleMapEntry<Tuple5> entry : midActionList) {
+            String cid = entry.key;
+            double tsHistory = Double.parseDouble(entry.value.f1);
+            double click = Double.parseDouble(entry.value.f2);
+            double conver = Double.parseDouble(entry.value.f3);
+            double d = (ts - tsHistory) / 3600 / 24;
+            if (!midTimeDiffMap.containsKey("timediff_view_" + cid)) {
+                midTimeDiffMap.put("timediff_view_" + cid, NumUtil.div(1, d));
+            }
+            if (!midTimeDiffMap.containsKey("timediff_click_" + cid) && click > 0) {
+                midTimeDiffMap.put("timediff_click_" + cid, NumUtil.div(1, d));
+            }
+            if (!midTimeDiffMap.containsKey("timediff_conver_" + cid) && conver > 0) {
+                midTimeDiffMap.put("timediff_conver_" + cid, NumUtil.div(1, d));
+            }
+        }
+        return midTimeDiffMap;
+    }
+
+    private Map<String, Double> parseC1FeatureListToActionStaticMap(List<TupleMapEntry<Tuple5>> midActionList) {
+        Map<String, Double> midActionStaticsMap = new HashMap<>();
+        for (TupleMapEntry<Tuple5> entry : midActionList) {
+            String cid = entry.key;
+            double click = Double.parseDouble(entry.value.f2);
+            double conver = Double.parseDouble(entry.value.f3);
+            double income = Double.parseDouble(entry.value.f4);
+
+            Double viewSum = midActionStaticsMap.getOrDefault("actionstatic_view_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_view_" + cid, 1 + viewSum);
+
+            Double clickSum = midActionStaticsMap.getOrDefault("actionstatic_click_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_click_" + cid, clickSum + click);
+
+            Double converSum = midActionStaticsMap.getOrDefault("actionstatic_conver_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_conver_" + cid, converSum + conver);
+
+            Double incomSum = midActionStaticsMap.getOrDefault("actionstatic_income_" + cid, 0.0);
+            midActionStaticsMap.put("actionstatic_income_" + cid, incomSum + income);
+        }
+
+        return midActionStaticsMap;
+    }
+
+    private Map<String, Map<String, Double>> parseD2FeatureMap(Map<String, String> d2Feature) {
+        Map<String, Map<String, Double>> vidRankMaps = new HashMap<>();
+        for (Map.Entry<String, String> entry : d2Feature.entrySet()) {
+            String key = entry.getKey();
+            String value = entry.getValue();
+            Map<String, Double> valueMap = Arrays.stream(value.split(","))
+                    .map(r -> r.split(":"))
+                    .collect(Collectors.toMap(rList -> rList[0], rList -> Double.parseDouble(rList[2])));
+            vidRankMaps.put(key, valueMap);
+        }
+        return vidRankMaps;
+    }
+
+    public Map<String, String> handleSceneFeature(long ts) {
+        Map<String, String> sceneFeatureMap = new HashMap<>();
+        sceneFeatureMap.put("hour_" + DateUtils.getHourByTimestamp(ts), "0.1");
+        sceneFeatureMap.put("dayofweek_" + DateUtils.getDayOrWeekByTimestamp(ts), "0.1");
+        return sceneFeatureMap;
+    }
+
+    private void readBucketFile() {
+        if (MapUtils.isNotEmpty(bucketsMap)) {
+            return;
+        }
+        synchronized (this) {
+            InputStream resourceStream = RankService683.class.getClassLoader().getResourceAsStream("20240718_ad_bucket_688.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) {
+                if (this.bucketsMap.containsKey(name) && this.bucketsLen.containsKey(name)) {
+                    double[] buckets = this.bucketsMap.get(name);
+                    double bucketNum = this.bucketsLen.get(name);
+                    Double scoreNew = 1.0 / bucketNum * (ExtractorUtils.findInsertPosition(buckets, score) + 1.0);
+                    newFeatureMap.put(name, String.valueOf(scoreNew));
+                } else {
+                    newFeatureMap.put(name, String.valueOf(score));
+                }
+            }
+        }
+
+        return newFeatureMap;
+    }
+
+    public static class Tuple5 {
+        public String f1;
+        public String f2;
+        public String f3;
+        public String f4;
+        public String f5;
+
+        public Tuple5(String f1, String f2, String f3, String f4, String f5) {
+            this.f1 = f1;
+            this.f2 = f2;
+            this.f3 = f3;
+            this.f4 = f4;
+            this.f5 = f5;
+        }
+    }
+
+    public static class TupleMapEntry<T> {
+        public String key;
+        public T value;
+
+        public TupleMapEntry(String key, T value) {
+            this.key = key;
+            this.value = value;
+        }
+    }
+
+    public static class Tuple2<F1, F2> {
+        public F1 f1;
+
+        public F2 f2;
+
+        public Tuple2(F1 first, F2 name) {
+            this.f1 = first;
+            this.f2 = name;
+        }
+
+    }
+}

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

@@ -4,6 +4,7 @@ import com.alibaba.fastjson.JSON;
 import com.google.gson.Gson;
 import com.tzld.piaoquan.ad.engine.commons.redis.AlgorithmRedisHelper;
 import com.tzld.piaoquan.ad.engine.commons.score.ScoreParam;
+import com.tzld.piaoquan.ad.engine.commons.util.ComparatorUtil;
 import com.tzld.piaoquan.ad.engine.service.score.dto.AdPlatformCreativeDTO;
 import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdRankItem;
 import org.apache.commons.lang3.StringUtils;
@@ -97,7 +98,7 @@ public class VideoAdThompsonScorerV2 {
             result.add(item);
         }
 
-        result.sort(equalsRandomComparator());
+        result.sort(ComparatorUtil.equalsRandomComparator());
         return result;
     }
 
@@ -263,7 +264,7 @@ public class VideoAdThompsonScorerV2 {
             result.add(item);
         }
 
-        result.sort(equalsRandomComparator());
+        result.sort(ComparatorUtil.equalsRandomComparator());
         return result;
     }
 
@@ -275,7 +276,7 @@ public class VideoAdThompsonScorerV2 {
 
         List<AdRankItem> result = new ArrayList<>(adIdList.size());
         this.calcScore(result, adIdList, 1d, creativeExpSum, videoCreativeExpSum, creativeStatisticsMap, videoCreativeStatisticsMap, exp669Param);
-        result.sort(equalsRandomComparator());
+        result.sort(ComparatorUtil.equalsRandomComparator());
 
         for (AdRankItem adRankItem : result) {
             adRankItem.setVideoId(param.getVideoId());
@@ -360,7 +361,7 @@ public class VideoAdThompsonScorerV2 {
 
         }
 
-        result.sort(equalsRandomComparator());
+        result.sort(ComparatorUtil.equalsRandomComparator());
         return result;
     }
 
@@ -470,21 +471,6 @@ public class VideoAdThompsonScorerV2 {
                 .getSum();
     }
 
-    private Comparator<AdRankItem> equalsRandomComparator() {
-        return new Comparator<AdRankItem>() {
-            @Override
-            public int compare(AdRankItem o1, AdRankItem o2) {
-                int comparison = o1.compareTo(o2);
-                if (comparison != 0) {
-                    return comparison;
-                }
-                // 数字相等时随机排列
-                return random.nextInt(3) - 1; // 产生-1、0或1的随机值
-            }
-        };
-    }
-
-
     private Map<String, Object> extMap(CreativeStatistic statistic, String abCode, Double cpa, Double viewThreshold, Double alpha, Double beta, Double beta_k) {
         Map<String, Object> map = new HashMap<>();
         if (Objects.nonNull(viewThreshold)) {
@@ -575,7 +561,7 @@ public class VideoAdThompsonScorerV2 {
             result.add(item);
 
         }
-        result.sort(equalsRandomComparator());
+        result.sort(ComparatorUtil.equalsRandomComparator());
         return result;
     }
 

ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/XGBoost351Scorer.java → ad-engine-service/src/main/java/com/tzld/piaoquan/ad/engine/service/score/XGBoostScorer683.java

@@ -4,7 +4,7 @@ package com.tzld.piaoquan.ad.engine.service.score;
 import com.tzld.piaoquan.ad.engine.commons.score.BaseXGBoostModelScorer;
 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.XGBoost351Model;
+import com.tzld.piaoquan.ad.engine.commons.score.model.XGBoostModel683;
 import com.tzld.piaoquan.ad.engine.commons.score.model.XGBoostModel;
 import com.tzld.piaoquan.recommend.feature.domain.ad.base.AdRankItem;
 import com.tzld.piaoquan.recommend.feature.domain.ad.base.UserAdFeature;
@@ -18,20 +18,20 @@ import java.util.*;
 import java.util.concurrent.*;
 
 
-public class XGBoost351Scorer extends BaseXGBoostModelScorer {
+public class XGBoostScorer683 extends BaseXGBoostModelScorer {
 
     private static final int LOCAL_TIME_OUT = 150;
-    private final static Logger LOGGER = LoggerFactory.getLogger(XGBoost351Scorer.class);
+    private final static Logger LOGGER = LoggerFactory.getLogger(XGBoostScorer683.class);
     private static final ExecutorService executorService = Executors.newFixedThreadPool(128);
 
 
-    public XGBoost351Scorer(ScorerConfigInfo configInfo) {
+    public XGBoostScorer683(ScorerConfigInfo configInfo) {
         super(configInfo);
     }
 
     @Override
     public void loadModel() {
-        doLoadModel(XGBoost351Model.class);
+        doLoadModel(XGBoostModel683.class);
     }
 
     @Override

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

@@ -1,6 +1,5 @@
 package com.tzld.piaoquan.ad.engine.service.score.impl;
 
-import com.alibaba.fastjson.JSON;
 import com.alibaba.fastjson.JSONObject;
 import com.tzld.piaoquan.ad.engine.commons.score.ScoreParam;
 import com.tzld.piaoquan.ad.engine.commons.score.ScorerUtils;
@@ -11,6 +10,7 @@ import com.tzld.piaoquan.ad.engine.service.predict.param.ThresholdPredictModelPa
 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.RankService683;
 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;
@@ -56,7 +56,9 @@ public class RankServiceImpl implements RankService {
     private AdCreativeFeatureContainer adCreativeFeatureContainer;
 
     @Autowired
-    private RankService680 fmRankService;
+    private RankService680 rankService680;
+    @Autowired
+    private RankService683 rankService683;
     @Autowired
     private TacticsAndLRModelScoreRankService tacticsAndFmModelScoreRankService;
 
@@ -81,6 +83,8 @@ public class RankServiceImpl implements RankService {
         Integer newExpGroup = requestParam.getNewExpGroup();
         if (AbUtil.isInAbExp(expCodeSet, appType, newExpGroup, fmModelScoreExpCode)) {
             return rankBy680(requestParam);
+        } else if (AbUtil.isInAbExp(expCodeSet, appType, newExpGroup, "683")) {
+            return rankBy683(requestParam);
         } else if (AbUtil.isInAbExp(expCodeSet, appType, newExpGroup, tacticsAndFmModelScoreExpCode)) {
             return rankBy679(requestParam);
         } else {
@@ -90,21 +94,18 @@ public class RankServiceImpl implements RankService {
 
     private AdRankItem rankBy680(RankRecommendRequestParam request) {
         ScoreParam scoreParam = RequestConvert.requestConvert(request);
-        List<AdRankItem> adRankItems = fmRankService.adItemRank(request, scoreParam);
-        // for (AdRankItem adRankItem : adRankItems) {
-        //     JSONObject json = new JSONObject();
-        //     json.put("scoreMap", adRankItem.getScoreMap());
-        //     json.put("allfeature", adRankItem.getFeatureMap());
-        //     json.put("metafeaturemap", adRankItem.getMetaFeatureMap());
-        //     json.put("cid", adRankItem.getAdId());
-        //     json.put("adverid", adRankItem.getAdVerId());
-        //     json.put("score", adRankItem.getScore());
-        //     log.info("adRankItem: {}", JSON.toJSONString(json));
-        // }
+        List<AdRankItem> adRankItems = rankService680.adItemRank(request, scoreParam);
         logHubService.scoreLogUpload(scoreParam, request.getAdIdList(), adRankItems, request, "680");
         return adRankItems.get(0);
     }
 
+    private AdRankItem rankBy683(RankRecommendRequestParam request) {
+        ScoreParam scoreParam = RequestConvert.requestConvert(request);
+        List<AdRankItem> adRankItems = rankService683.adItemRank(request, scoreParam);
+        logHubService.scoreLogUpload(scoreParam, request.getAdIdList(), adRankItems, request, "683");
+        return adRankItems.get(0);
+    }
+
     private AdRankItem rankBy679(RankRecommendRequestParam requestParam) {
         ScoreParam scoreParam = RequestConvert.requestConvert(requestParam);
         List<AdRankItem> adRankItems = tacticsAndFmModelScoreRankService.adItemRank(requestParam, scoreParam);