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TRACE model predicts delayed conversion rates using post-click behavior

Researchers have introduced TRACE, a novel method for predicting online conversion rates when feedback is delayed. TRACE addresses the challenge by analyzing post-click user behavior trajectories rather than relying solely on final outcomes or traditional delay modeling. The system dynamically refines predictions based on accumulated feedback and uses a reliability-gated completer to leverage full-lifecycle data for improved accuracy on unrevealed samples. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Improves prediction accuracy in scenarios with delayed feedback, potentially benefiting e-commerce and advertising platforms.

RANK_REASON Academic paper on a novel method for delayed conversion rate prediction.

Read on arXiv cs.LG →

COVERAGE [1]

  1. arXiv cs.LG TIER_1 · Xinyue Zhang, Yuanhao Ding, Xiang Ao ·

    Follow the TRACE: Exploiting Post-Click Trajectories for Online Delayed Conversion Rate Prediction

    arXiv:2604.23197v1 Announce Type: new Abstract: Delayed feedback poses a core challenge for online CVR prediction, forcing a trade-off between label accuracy and data freshness. Existing methods address this through delay modeling or sample reweighting, yet neglect how post-click…