Personalized Task Dependency Graphs for Mitigating Signal Erosion in Multi-Task Recommendation

2026-09-04

AI Summary

为解决多任务推荐中信号衰减问题，提出个性化任务依赖图(PTDG)，通过动态调整任务依赖强度和自适应信息传播，提升稀疏转化任务性能。

Abstract

Optimizing multiple conversion objectives is a core challenge in industrial recommendation, often limited by signal erosion in rigid architectures. Existing Multi-Task Learning (MTL) methods typically enforce uniform dependency strengths across a static conversion funnel, overlooking how task correlations naturally vary based on item characteristics. Hierarchical message passing along these fixed chains leads to cumulative signal attenuation, which degrades performance on sparse, deep-funnel objectives. To address this, we propose the Personalized Task Dependency Graphs (PTDG). While respecting necessary physical causal constraints (e.g., Click -> Pay), PTDG dynamically "rewires" the intensity of dependency pathways for each item via low-rank approximation to ensure structural robustness. We implement a GCN-based propagation with hard causal masking to establish adaptive information shortcuts. Additionally, we introduce an Adaptive Progressive Masking (APM) strategy that decouples shared parameters according to task sparsity, helping to stabilize optimization. Experiments on KuaiRand1K and an industrial dataset show that PTDG significantly improves AUC on sparse conversion tasks by up to 1.45%, while maintaining comparable performance on dense objectives. Online A/B testing shows PTDG improves Conversion Rate (CVR) by 1.2% and effective Cost Per Mille (eCPM) by 1.9% relative to the baseline.

Problem

• signal erosion
• multi-task recommendation
• conversion objectives
• task dependency

Innovation

• Personalized Task Dependency Graphs
• Low-rank Approximation
• Adaptive Progressive Masking