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@@ -180,11 +180,16 @@ public class RankStrategyBy688 extends RankStrategyBasic {
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this.handleD1Feature(d1Feature, cidFeatureMap);
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this.handleD2Feature(vidRankMaps, cidFeatureMap, cidStr);
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this.handleH1AndH2Feature(skuFeature, adVerFeature, cidFeatureMap);
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-
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cidFeatureMap.put("cid", dto.getCreativeId() != null ? String.valueOf(dto.getCreativeId()) : "");
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cidFeatureMap.put("adid", dto.getAdId() != null ? String.valueOf(dto.getAdId()) : "");
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cidFeatureMap.put("adverid", dto.getAdVerId() != null ? dto.getAdVerId() : "");
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cidFeatureMap.put("profession", dto.getProfession() != null ? dto.getProfession() : "");
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+ //DNN模型没训练过的cid才不传入广告相关的稀疏特征
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+ if (CollectionUtils.isNotEmpty(DnnCidDataHelper.getCidSet()) && !DnnCidDataHelper.getCidSet().contains(adRankItem.getAdId())) {
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+ cidFeatureMap.put("cid", "");
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+ cidFeatureMap.put("adid", "");
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+ cidFeatureMap.put("adverid", "");
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+ }
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return adRankItem;
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} finally {
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cdl1.countDown();
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@@ -264,17 +269,6 @@ public class RankStrategyBy688 extends RankStrategyBasic {
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for (AdRankItem item : result) {
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double originalScore = item.getLrScore();
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double calibratedScore = originalScore / (originalScore + (1 - originalScore) / negSampleRate);
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- // 该创意尚未在模型中训练，打分不可靠
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- if (CollectionUtils.isNotEmpty(DnnCidDataHelper.getCidSet()) && !DnnCidDataHelper.getCidSet().contains(item.getAdId())) {
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- Map<String, Map<String, String>> cidFeature = allCidFeature.getOrDefault(String.valueOf(item.getAdId()), new HashMap<>());
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- Map<String, String> b3Feature = cidFeature.getOrDefault("alg_cid_feature_cid_action", new HashMap<>());
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- double view = Double.parseDouble(b3Feature.getOrDefault("ad_view_14d", "0"));
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- double conver = Double.parseDouble(b3Feature.getOrDefault("ad_conversion_14d", "0"));
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- double smoothCxr = NumUtil.divSmoothV1(conver, view, 1.64);
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- if (smoothCxr < calibratedScore) {
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- calibratedScore = smoothCxr;
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- }
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- }
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item.setLrScore(calibratedScore);
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item.getScoreMap().put("originCtcvrScore", originalScore);
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item.getScoreMap().put("ctcvrScore", calibratedScore);
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