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@@ -1,115 +1,422 @@
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package com.tzld.piaoquan.recommend.server.service.rank.strategy;
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import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
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+import com.tzld.piaoquan.recommend.server.common.ThreadPoolFactory;
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+import com.tzld.piaoquan.recommend.server.common.base.RankItem;
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import com.tzld.piaoquan.recommend.server.model.Video;
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import com.tzld.piaoquan.recommend.server.service.FeatureService;
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import com.tzld.piaoquan.recommend.server.service.rank.RankParam;
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+import com.tzld.piaoquan.recommend.server.service.rank.extractor.ExtractorUtils;
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+import com.tzld.piaoquan.recommend.server.service.recall.strategy.*;
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+import com.tzld.piaoquan.recommend.server.service.score.ScorerUtils;
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+import com.tzld.piaoquan.recommend.server.util.CommonCollectionUtils;
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import lombok.extern.slf4j.Slf4j;
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+import org.apache.commons.collections4.MapUtils;
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+import org.apache.commons.lang3.StringUtils;
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+import org.apache.commons.math3.util.Pair;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.stereotype.Service;
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-import java.util.ArrayList;
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-import java.util.HashMap;
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-import java.util.List;
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-import java.util.Map;
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+import java.util.*;
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+import java.util.concurrent.Future;
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+import java.util.concurrent.TimeUnit;
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+import java.util.stream.Collectors;
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@Service
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@Slf4j
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public class RankStrategy4RegionMergeModelV566 extends RankStrategy4RegionMergeModelBasic {
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@ApolloJsonValue("${rank.score.merge.weightv566:}")
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private Map<String, Double> mergeWeight;
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-
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- @Autowired
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- private RankStrategy4RegionMergeModelV563 modelV563Service;
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-
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@Autowired
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private FeatureService featureService;
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@Override
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public List<Video> mergeAndRankRovRecall(RankParam param) {
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Map<String, Double> mergeWeight = this.mergeWeight != null ? this.mergeWeight : new HashMap<>(0);
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+ //-------------------融-------------------
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+ //-------------------合-------------------
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+ //-------------------逻-------------------
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+ //-------------------辑-------------------
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+
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+ List<Video> oldRovs = new ArrayList<>();
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+ oldRovs.addAll(extractAndSort(param, RegionHRecallStrategy.PUSH_FORM));
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+ oldRovs.addAll(extractAndSort(param, RegionHDupRecallStrategy.PUSH_FORM));
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+ oldRovs.addAll(extractAndSort(param, Region24HRecallStrategy.PUSH_FORM));
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+ oldRovs.addAll(extractAndSort(param, RegionRelative24HRecallStrategy.PUSH_FORM));
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+ oldRovs.addAll(extractAndSort(param, RegionRelative24HDupRecallStrategy.PUSH_FORM));
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+ removeDuplicate(oldRovs);
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+ int sizeReturn = param.getSize();
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+ List<Video> v0 = oldRovs.size() <= sizeReturn
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+ ? oldRovs
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+ : oldRovs.subList(0, sizeReturn);
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+ Set<Long> setVideo = new HashSet<>();
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+ this.duplicate(setVideo, v0);
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+ setVideo.addAll(v0.stream().map(Video::getVideoId).collect(Collectors.toSet()));
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+ List<Video> rovRecallRank = new ArrayList<>(v0);
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+ //-------------------return相似召回------------------
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+ List<Video> v6 = extractAndSort(param, ReturnVideoRecallStrategy.PUSH_FORM);
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+ v6 = v6.stream().filter(r -> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
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+ v6 = v6.subList(0, Math.min(mergeWeight.getOrDefault("v6", 5.0).intValue(), v6.size()));
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+ rovRecallRank.addAll(v6);
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+ setVideo.addAll(v6.stream().map(Video::getVideoId).collect(Collectors.toSet()));
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+ //-------------------新地域召回------------------
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+ List<Video> v1 = extractAndSort(param, RegionRealtimeRecallStrategyV1.PUSH_FORM);
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+ v1 = v1.stream().filter(r -> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
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+ v1 = v1.subList(0, Math.min(mergeWeight.getOrDefault("v1", 5.0).intValue(), v1.size()));
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+ rovRecallRank.addAll(v1);
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+ setVideo.addAll(v1.stream().map(Video::getVideoId).collect(Collectors.toSet()));
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+ //-------------------scene cf rovn------------------
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+ List<Video> sceneCFRovn = extractAndSort(param, SceneCFRovnRecallStrategy.PUSH_FORM);
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+ sceneCFRovn = sceneCFRovn.stream().filter(r -> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
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+ sceneCFRovn = sceneCFRovn.subList(0, Math.min(mergeWeight.getOrDefault("sceneCFRovn", 5.0).intValue(), sceneCFRovn.size()));
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+ rovRecallRank.addAll(sceneCFRovn);
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+ setVideo.addAll(sceneCFRovn.stream().map(Video::getVideoId).collect(Collectors.toSet()));
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+ //-------------------scene cf rosn------------------
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+ List<Video> sceneCFRosn = extractAndSort(param, SceneCFRosnRecallStrategy.PUSH_FORM);
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+ sceneCFRosn = sceneCFRosn.stream().filter(r -> !setVideo.contains(r.getVideoId())).collect(Collectors.toList());
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+ sceneCFRosn = sceneCFRosn.subList(0, Math.min(mergeWeight.getOrDefault("sceneCFRosn", 5.0).intValue(), sceneCFRosn.size()));
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+ rovRecallRank.addAll(sceneCFRosn);
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+ setVideo.addAll(sceneCFRosn.stream().map(Video::getVideoId).collect(Collectors.toSet()));
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+
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+ //-------------------head cate2 of rovn------------------
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+ List<Video> headCate2Rov = extractAndSort(param, HeadCate2AndChannelRovRecallStrategy.PUSH_FROM);
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+ // 视频去重
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+ removeDuplicate(headCate2Rov);
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+ headCate2Rov = headCate2Rov.stream().filter(o -> !setVideo.contains(o.getVideoId())).collect(Collectors.toList());
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+ headCate2Rov = headCate2Rov.subList(0, Math.min(mergeWeight.getOrDefault("headCate2AndChannelRov", 5.0).intValue(), headCate2Rov.size()));
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+ rovRecallRank.addAll(headCate2Rov);
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+ setVideo.addAll(headCate2Rov.stream().map(Video::getVideoId).collect(Collectors.toSet()));
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+
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+ //-------------------排-------------------
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+ //-------------------序-------------------
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+ //-------------------逻-------------------
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+ //-------------------辑-------------------
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+
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+ // 1 批量获取特征 省份参数要对齐 headvid 要传递过来!
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+ List<String> vids = CommonCollectionUtils.toListDistinct(rovRecallRank, v -> String.valueOf(v.getVideoId()));
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+
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+ // k1:视频、k2:表、k3:特征、v:特征值
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+ String provinceCn = param.getProvince().replaceAll("省$", "");
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+ String headVid = String.valueOf(param.getHeadVid());
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+ FeatureService.Feature feature = featureService.getFeature(param.getMid(), vids,
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+ String.valueOf(param.getAppType()), provinceCn, headVid);
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+ Map<String, Map<String, String>> featureOriginUser = feature.getUserFeature();
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+ Map<String, Map<String, Map<String, String>>> featureOriginVideo = feature.getVideoFeature();
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+
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- List<Video> result = modelV563Service.mergeAndRankRovRecall(param);
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- if (null != result && !result.isEmpty()) {
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- try {
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- Integer versionCode = param.getVersionCode();
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- int orderVersionCode = mergeWeight.getOrDefault("orderVersionCode", 1500D).intValue();
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- if (null != versionCode && versionCode == orderVersionCode) {
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- int minBid = mergeWeight.getOrDefault("minBid", 39D).intValue();
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- int keepPageNum = mergeWeight.getOrDefault("keepPageNum", 1D).intValue();
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- if (param.getPageNum() > keepPageNum && highlyActiveUser(param.getMid(), minBid)) {
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- int group1Size = mergeWeight.getOrDefault("group1Size", 4D).intValue();
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- int group2Size = mergeWeight.getOrDefault("group2Size", 8D).intValue();
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- int group3Size = mergeWeight.getOrDefault("group3Size", 0D).intValue();
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- return resort(group1Size, group2Size, group3Size, result);
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+ // 2 特征处理
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+ Map<String, Double> userFeatureMapDouble = new HashMap<>();
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+ String mid = param.getMid();
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+ Map<String, String> c1 = featureOriginUser.getOrDefault("alg_mid_feature_play", new HashMap<>());
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+ Map<String, String> c2 = featureOriginUser.getOrDefault("alg_mid_feature_share_and_return", new HashMap<>());
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+ Map<String, String> c3 = featureOriginUser.getOrDefault("alg_mid_feature_play_tags", new HashMap<>());
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+ Map<String, String> c4 = featureOriginUser.getOrDefault("alg_mid_feature_return_tags", new HashMap<>());
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+ Map<String, String> c5 = featureOriginUser.getOrDefault("alg_mid_feature_share_tags", new HashMap<>());
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+ Map<String, String> c6 = featureOriginUser.getOrDefault("alg_mid_feature_feed_exp_share_tags_v2", new HashMap<>());
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+ Map<String, String> c7 = featureOriginUser.getOrDefault("alg_mid_feature_feed_exp_return_tags_v2", new HashMap<>());
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+ Map<String, String> c8 = featureOriginUser.getOrDefault("alg_mid_feature_sharecf", new HashMap<>());
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+ Map<String, String> c9 = featureOriginUser.getOrDefault("alg_mid_feature_returncf", new HashMap<>());
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+
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+ if (!c1.isEmpty()) {
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+ userFeatureMapDouble.put("playcnt_6h", Double.parseDouble(c1.getOrDefault("playcnt_6h", "0")));
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+ userFeatureMapDouble.put("playcnt_1d", Double.parseDouble(c1.getOrDefault("playcnt_1d", "0")));
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+ userFeatureMapDouble.put("playcnt_3d", Double.parseDouble(c1.getOrDefault("playcnt_3d", "0")));
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+ userFeatureMapDouble.put("playcnt_7d", Double.parseDouble(c1.getOrDefault("playcnt_7d", "0")));
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+ }
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+ if (!c2.isEmpty()) {
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+ userFeatureMapDouble.put("share_pv_12h", Double.parseDouble(c2.getOrDefault("share_pv_12h", "0")));
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+ userFeatureMapDouble.put("share_pv_1d", Double.parseDouble(c2.getOrDefault("share_pv_1d", "0")));
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+ userFeatureMapDouble.put("share_pv_3d", Double.parseDouble(c2.getOrDefault("share_pv_3d", "0")));
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+ userFeatureMapDouble.put("share_pv_7d", Double.parseDouble(c2.getOrDefault("share_pv_7d", "0")));
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+ userFeatureMapDouble.put("return_uv_12h", Double.parseDouble(c2.getOrDefault("return_uv_12h", "0")));
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+ userFeatureMapDouble.put("return_uv_1d", Double.parseDouble(c2.getOrDefault("return_uv_1d", "0")));
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+ userFeatureMapDouble.put("return_uv_3d", Double.parseDouble(c2.getOrDefault("return_uv_3d", "0")));
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+ userFeatureMapDouble.put("return_uv_7d", Double.parseDouble(c2.getOrDefault("return_uv_7d", "0")));
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+ }
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+
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+ Map<String, String> c34567Map = new HashMap<>(15);
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+ List<Tuple2> tmpList0 = Arrays.asList(
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+ new Tuple2(c3, "c3_feature"),
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+ new Tuple2(c4, "c4_feature"),
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+ new Tuple2(c5, "c5_feature"),
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+ new Tuple2(c6, "c6_feature"),
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+ new Tuple2(c7, "c7_feature")
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+ );
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+ for (Tuple2 tuple2 : tmpList0) {
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+ for (String key_time : Arrays.asList("tags_1d", "tags_3d", "tags_7d")) {
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+ String tags = tuple2.first.getOrDefault(key_time, "");
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+ if (!tags.isEmpty()) {
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+ c34567Map.put(tuple2.name + "_" + key_time, tags);
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+ }
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+ }
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+ }
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+
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+ Map<String, Map<String, String[]>> c89Map = new HashMap<>(4);
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+ List<Tuple2> tmpList1 = Arrays.asList(
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+ new Tuple2(c8, "c8_feature"),
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+ new Tuple2(c9, "c9_feature")
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+ );
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+ for (Tuple2 tuple2 : tmpList1) {
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+ for (String key_action : Arrays.asList("share", "return")) {
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+ String cfListStr = tuple2.first.getOrDefault(key_action, "");
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+ if (!cfListStr.isEmpty()) {
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+ Map<String, String[]> cfMap = new HashMap<>();
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+ String[] entries = cfListStr.split(",");
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+ for (String entry : entries) {
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+ String[] rList = entry.split(":");
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+ if (rList.length >= 4) { // 确保分割后有四个元素
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+ String key = rList[0];
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+ String value1 = rList[1];
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+ String value2 = rList[2];
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+ String value3 = rList[3];
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+ String[] strs = {value1, value2, value3};
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+ cfMap.put(key, strs);
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+ }
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}
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+ c89Map.put(tuple2.name + "_" + key_action, cfMap);
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}
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- } catch (Exception e) {
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- log.error("diversity resort error", e);
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}
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}
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- return result;
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- }
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- private List<Video> resort(int group1Size, int group2Size, int group3Size, List<Video> videos) {
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- // split
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- List<Video> group1 = new ArrayList<>();
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- List<Video> group2 = new ArrayList<>();
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- List<Video> group3 = new ArrayList<>();
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- List<Video> group4 = new ArrayList<>();
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- for (int i = 0; i < videos.size(); i++) {
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- if (i < group1Size) {
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- group1.add(videos.get(i));
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- } else if (i < group2Size) {
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- group2.add(videos.get(i));
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- } else if (i < group3Size) {
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- group3.add(videos.get(i));
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- } else {
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- group4.add(videos.get(i));
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+
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+ List<RankItem> rankItems = CommonCollectionUtils.toList(rovRecallRank, RankItem::new);
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+ for (RankItem item : rankItems) {
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+ Map<String, Double> featureMap = new HashMap<>();
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+ String vid = item.getVideoId() + "";
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+ Map<String, String> b1 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_all_exp_v2", new HashMap<>());
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+ Map<String, String> b2 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_all_share", new HashMap<>());
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+ Map<String, String> b3 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_all_return", new HashMap<>());
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+ Map<String, String> b6 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_exp2share_v2", new HashMap<>());
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+ Map<String, String> b7 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_share2return", new HashMap<>());
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+
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+ Map<String, String> b8 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_noflow_exp_v2", new HashMap<>());
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+ Map<String, String> b9 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_noflow_root_share_v2", new HashMap<>());
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+ Map<String, String> b10 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_noflow_root_return_v2", new HashMap<>());
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+ Map<String, String> b11 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_flow_exp_v2", new HashMap<>());
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+ Map<String, String> b12 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_flow_root_share_v2", new HashMap<>());
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+ Map<String, String> b13 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_flow_root_return_v2", new HashMap<>());
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+ Map<String, String> b17 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_province_exp_v2", new HashMap<>());
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+ Map<String, String> b18 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_province_root_share_v2", new HashMap<>());
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+ Map<String, String> b19 = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_feed_province_root_return_v2", new HashMap<>());
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+
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+ List<Tuple4> originData = Arrays.asList(
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+ new Tuple4(b1, b2, b3, "b123"),
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+ new Tuple4(b1, b6, b7, "b167"),
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+ new Tuple4(b8, b9, b10, "b8910"),
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+ new Tuple4(b11, b12, b13, "b111213"),
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+ new Tuple4(b17, b18, b19, "b171819")
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+ );
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+
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+ for (Tuple4 tuple4 : originData) {
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+ for (String prefix2 : Arrays.asList("1h", "2h", "3h", "4h", "12h", "1d", "3d", "7d")) {
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+ double exp = tuple4.first.isEmpty() ? 0 : Double.parseDouble(tuple4.first.getOrDefault("exp_pv_" + prefix2, "0.0"));
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+ double share = tuple4.second.isEmpty() ? 0 : Double.parseDouble(tuple4.second.getOrDefault("share_pv_" + prefix2, "0.0"));
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+ double returns = tuple4.third.isEmpty() ? 0 : Double.parseDouble(tuple4.third.getOrDefault("return_uv_" + prefix2, "0.0"));
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+
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+ double f1 = ExtractorUtils.calDiv(share, exp);
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+ double f2 = ExtractorUtils.calLog(share);
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+ double f3 = ExtractorUtils.calDiv(returns, exp);
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+ double f4 = ExtractorUtils.calLog(returns);
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+ double f5 = f3 * f4;
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+
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+ String key1 = tuple4.name + "_" + prefix2 + "_" + "STR";
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+ String key2 = tuple4.name + "_" + prefix2 + "_" + "log(share)";
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+ String key3 = tuple4.name + "_" + prefix2 + "_" + "ROV";
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+ String key4 = tuple4.name + "_" + prefix2 + "_" + "log(return)";
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+ String key5 = tuple4.name + "_" + prefix2 + "_" + "ROV*log(return)";
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+
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+ featureMap.put(key1, f1);
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+ featureMap.put(key2, f2);
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+ featureMap.put(key3, f3);
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+ featureMap.put(key4, f4);
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+ featureMap.put(key5, f5);
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+ }
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}
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+
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+ Map<String, String> videoInfo = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_basic_info", new HashMap<>());
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+ featureMap.put("total_time", Double.parseDouble(videoInfo.getOrDefault("total_time", "0")));
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+ featureMap.put("bit_rate", Double.parseDouble(videoInfo.getOrDefault("bit_rate", "0")));
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+
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|
|
+ 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 = ExtractorUtils.funcC34567ForTagsNew(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;
|
|
|
}
|
|
|
|
|
|
- // merge
|
|
|
- List<List<Video>> groupList = new ArrayList<>();
|
|
|
- groupList.add(group1);
|
|
|
- groupList.add(group2);
|
|
|
- groupList.add(group3);
|
|
|
- List<Video> result = fillVideo(groupList);
|
|
|
- result.addAll(group4);
|
|
|
- return result;
|
|
|
- }
|
|
|
+ // 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;
|
|
|
|
|
|
- private List<Video> fillVideo(List<List<Video>> groupList) {
|
|
|
- List<Video> result = new ArrayList<>();
|
|
|
- int maxSize = getMaxSize(groupList);
|
|
|
- for (int i = 0; i < maxSize; i++) {
|
|
|
- for (List<Video> group : groupList) {
|
|
|
- if (null != group && i < group.size()) {
|
|
|
- result.add(group.get(i));
|
|
|
+ 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:");
|
|
|
+
|
|
|
+ // 获取权重
|
|
|
+ Map<String, Double> cate2Coefficient = new HashMap<>();
|
|
|
+ double cate2CoefficientFunc = mergeWeight.getOrDefault("cate2CoefficientFunc", 0d);
|
|
|
+ if (cate2CoefficientFunc == 1d) {
|
|
|
+ String headVidStr = String.valueOf(param.getHeadVid());
|
|
|
+ String mergeCate2 = this.findVideoMergeCate2(featureOriginVideo, headVidStr);
|
|
|
+ Double length = mergeWeight.getOrDefault("cate2CoefficientLength", 10000d);
|
|
|
+ Map<String, Double> simCateScore = this.findSimCateScore(mergeCate2, param.getChannelName(), length.intValue());
|
|
|
+ cate2Coefficient.putAll(simCateScore);
|
|
|
+ }
|
|
|
+
|
|
|
+ Double cate2CoefficientDenominator = mergeWeight.getOrDefault("cate2CoefficientDenominator", 1d);
|
|
|
+
|
|
|
+ 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);
|
|
|
+
|
|
|
+ String vidMergeCate2 = this.findVideoMergeCate2(featureOriginVideo, String.valueOf(item.getVideoId()));
|
|
|
+ Double scoreCoefficient = cate2Coefficient.getOrDefault(vidMergeCate2, 0d);
|
|
|
+ item.getScoresMap().put("scoreCoefficient", scoreCoefficient);
|
|
|
+ item.getScoresMap().put("cate2CoefficientDenominator", cate2CoefficientDenominator);
|
|
|
+
|
|
|
+ score = fmRov * (0.1 + hasReturnRovScore) * (0.1 + vor) * (1 + scoreCoefficient / cate2CoefficientDenominator);
|
|
|
+
|
|
|
+ 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;
|
|
|
}
|
|
|
|
|
|
- private int getMaxSize(List<List<Video>> groupList) {
|
|
|
- int maxSize = 0;
|
|
|
- if (null != groupList) {
|
|
|
- for (List<Video> group : groupList) {
|
|
|
- if (null != group) {
|
|
|
- maxSize = Math.max(maxSize, group.size());
|
|
|
- }
|
|
|
- }
|
|
|
+ private Map<String, Double> findSimCateScore(String headCate2, String channelName, int length) {
|
|
|
+ if (StringUtils.isBlank(headCate2)) {
|
|
|
+ return new HashMap<>();
|
|
|
}
|
|
|
- return maxSize;
|
|
|
+
|
|
|
+ String redisKey = String.format("alg_recsys_channel_good_cate_pair_list:%s:%s", channelName, headCate2);
|
|
|
+ String cate2Value = redisTemplate.opsForValue().get(redisKey);
|
|
|
+ if (StringUtils.isEmpty(cate2Value)) {
|
|
|
+ return new HashMap<>();
|
|
|
+ }
|
|
|
+
|
|
|
+ return this.parsePair(cate2Value, length);
|
|
|
}
|
|
|
|
|
|
- private boolean highlyActiveUser(String mid, int minBid) {
|
|
|
- Map<String, String> userInfo = featureService.getUserInfo("alg_recsys_feature_user_active_level", mid);
|
|
|
- if (null != userInfo && userInfo.containsKey("bid")) {
|
|
|
- int bid = Integer.parseInt(userInfo.get("bid"));
|
|
|
- return bid >= minBid;
|
|
|
+ private Map<String, Double> parsePair(String value, int length) {
|
|
|
+ if (StringUtils.isBlank(value)) {
|
|
|
+ return new HashMap<>();
|
|
|
}
|
|
|
- return false;
|
|
|
+
|
|
|
+ String[] split = value.split("\t");
|
|
|
+ if (split.length != 2) {
|
|
|
+ return new HashMap<>();
|
|
|
+ }
|
|
|
+
|
|
|
+ String[] valueList = split[0].trim().split(",");
|
|
|
+ String[] scoreList = split[1].trim().split(",");
|
|
|
+ if (valueList.length != scoreList.length) {
|
|
|
+ return new HashMap<>();
|
|
|
+ }
|
|
|
+
|
|
|
+ int minLength = Math.min(length, valueList.length);
|
|
|
+ Map<String, Double> resultMap = new HashMap<>();
|
|
|
+ for (int i = 0; i < minLength; i++) {
|
|
|
+ resultMap.put(valueList[i].trim(), Double.parseDouble(scoreList[i].trim()));
|
|
|
+ }
|
|
|
+
|
|
|
+ return resultMap;
|
|
|
+ }
|
|
|
+
|
|
|
+ private String findVideoMergeCate2(Map<String, Map<String, Map<String, String>>> featureOriginVideo, String vid) {
|
|
|
+ Map<String, String> videoInfo = featureOriginVideo.getOrDefault(vid, new HashMap<>()).getOrDefault("alg_vid_feature_basic_info", new HashMap<>());
|
|
|
+ return videoInfo.get("merge_second_level_cate");
|
|
|
}
|
|
|
-}
|
|
|
+}
|