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@@ -22,6 +22,8 @@ public class ModelUserLayerDataHelper {
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private static final String redisKey = "ad:engine:strategy:model_ctcvr_calibration_v1";
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private static final String keyFormat = "%s:%s:%s:%s:%s:%s:%s";
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private static final String SUM = "sum";
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+ /** 曝光大于该阈值才使用当前层校准，否则回退下一层 */
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+ private static final double MIN_CALIBRATION_EXPOSURE = 5000d;
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private volatile static Map<String, String> dataMap = Collections.emptyMap();
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@@ -46,6 +48,7 @@ public class ModelUserLayerDataHelper {
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/**
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* 按粒度回退取校准数据，并返回命中的校准层：
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* l1=agent+customer 粒度，l2=profession 粒度，l3=layer 粒度。
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+ * 当前层曝光必须大于 {@link #MIN_CALIBRATION_EXPOSURE}，否则回退下一层。
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* key/value 均以 ':' 分隔；
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* value: exposure_cnt:conversion_cnt:pred_conversion_cnt:real_ctcvr:pred_ctcvr:copc
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*/
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@@ -62,21 +65,21 @@ public class ModelUserLayerDataHelper {
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String key = String.format(keyFormat, model, landingPageType, targetingConversion, layer, profession, agentId, customerId);
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CalibrationData data = parseValue(dataMap.get(key));
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- if (data != null) {
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+ if (isExposureEnough(data)) {
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data.setLayer("l1");
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return data;
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}
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key = String.format(keyFormat, model, landingPageType, targetingConversion, layer, profession, SUM, SUM);
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data = parseValue(dataMap.get(key));
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- if (data != null) {
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+ if (isExposureEnough(data)) {
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data.setLayer("l2");
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return data;
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}
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key = String.format(keyFormat, model, landingPageType, targetingConversion, layer, SUM, SUM, SUM);
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data = parseValue(dataMap.get(key));
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- if (data != null) {
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+ if (isExposureEnough(data)) {
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data.setLayer("l3");
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return data;
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}
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@@ -84,6 +87,10 @@ public class ModelUserLayerDataHelper {
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return null;
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}
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+ private static boolean isExposureEnough(CalibrationData data) {
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+ return data != null && data.getExposureCnt() != null && data.getExposureCnt() > MIN_CALIBRATION_EXPOSURE;
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+ }
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+
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private static CalibrationData parseValue(String value) {
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if (value == null || value.trim().isEmpty()) {
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return null;
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