用时间聚合特征提升点击率预测，效果显著且稳定。
Time Aggregation Features for XGBoost Models
- 采用滚动尾部窗口聚合历史行为数据，避免信息泄露。
- 相比基准方法，ROC AUC提升0.0066至0.0082，PR AUC提升0.0084至0.0094。
- 推荐使用尾部窗口，事件计数窗口可作为微调选项。
本文研究了在点击率预测中为XGBoost模型设计的时间聚合特征。实验基于Avazu数据集，采用严格的时序划分和无前瞻特征约束，即第H小时的特征仅使用严格早于H小时的曝光数据。对比强基线（时间感知目标编码）与多种时间聚合窗口设计（滚动尾部、事件计数、间隔、分桶），在确定性的10%样本上进行两轮滚动尾部交叉验证，结果表明：尾部窗口使ROC AUC提升约0.0066–0.0082，PR AUC提升约0.0084–0.0094；事件计数窗口虽有小幅持续改进，但增益有限；间隔与分桶窗口表现均弱于尾部窗口。结论支持以尾部窗口为默认方案，仅在追求微小ROC AUC提升时考虑事件计数窗口。
原文摘要 · Abstract (English)
This paper studies time aggregation features for XGBoost models in click-through rate prediction. The setting is the Avazu click-through rate prediction dataset with strict out-of-time splits and a no-lookahead feature constraint. Features for hour H use only impressions from hours strictly before H. This paper compares a strong time-aware target encoding baseline to models augmented with entity history time aggregation under several window designs. Across two rolling-tail folds on a deterministic ten percent sample, a trailing window specification improves ROC AUC by about 0.0066 to 0.0082 and PR AUC by about 0.0084 to 0.0094 relative to target encoding alone. Within the time aggregation design grid, event count windows provide the only consistent improvement over trailing windows, and the gain is small. Gap windows and bucketized windows underperform simple trailing windows in this dataset and protocol. These results support a practical default of trailing windows, with an optional event count window when marginal ROC AUC gains matter.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。