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

arXiv: https://arxiv.org/abs/2604.23197 · HTML: https://mlsanity.com/paper/2604.23197/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 25 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://mlsanity.com/paper/2204.05125/) (2022)
- [Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction](https://mlsanity.com/paper/1907.06558/) (2019)
- [Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising](https://mlsanity.com/paper/1907.10235/) (2019)
- [Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling](https://mlsanity.com/paper/2104.14121/) (2021)
- [A Nonparametric Delayed Feedback Model for Conversion Rate Prediction](https://mlsanity.com/paper/1802.00255/) (2018)
- [Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions](https://mlsanity.com/paper/1905.10713/) (2019)
- [Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling](https://mlsanity.com/paper/2012.03245/) (2020)
- [A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback](https://mlsanity.com/paper/2002.02068/) (2020)
- [Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](https://mlsanity.com/paper/2202.06472/) (2022)
- [Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction](https://mlsanity.com/paper/2011.11826/) (2020)
- [Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction](https://mlsanity.com/paper/2307.12756/) (2023)
- [Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems](https://mlsanity.com/paper/2206.00407/) (2022)
- [Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback](https://mlsanity.com/paper/2108.06167/) (2021)

## Abstract (arXiv)

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 behaviors evolve over the observation period. To overcome this limitation, we formalize this evolution as feedback trajectory and propose TRACE. Instead of forcing hard labels on unrevealed samples, our method evaluates how well the accumulated feedback status aligns with conversion versus non-conversion, dynamically refining posteriors without waiting for final outcomes. To counteract early-stage trajectory sparsity, we further design a reliability-gated retrospective completer that leverages full-lifecycle data to provide adaptive posterior guidance for unrevealed samples. Extensive experiments validate TRACE's superiority over state-of-the-art baselines and confirm the retrospective completion module as a model-agnostic enhancer for existing systems. Our code is available at https://github.com/LunaZhangxy/TRACE.