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

2026-04-25

AI Summary

This work addresses the fundamental trade-off between label accuracy and data freshness in online conversion rate prediction, which arises due to delayed conversions. The authors propose TRACE, a novel framework that formalizes post-click user behavior as feedback trajectories, enabling dynamic modeling of conversion likelihood without waiting for final conversion outcomes. Central to TRACE are two key innovations: a reliability-gated retrospective completion mechanism and dynamic posterior optimization, which together provide adaptive guidance on unobserved samples. Designed as a model-agnostic module, TRACE can seamlessly enhance existing prediction systems. Extensive experiments demonstrate that the proposed approach significantly outperforms current state-of-the-art baselines, confirming its effectiveness and broad applicability.

Technology Category

• Machine Learning: Online Learning & Bandits
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 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.

Problem

• delayed feedback
• conversion rate prediction
• post-click behavior
• online learning
• trajectory modeling

Innovation

• delayed conversion rate prediction
• post-click trajectory
• feedback trajectory
• retrospective completion
• model-agnostic enhancement