Structured Task Alignment for Multi-Objective Learning to Rank

Abstract

Industrial recommender systems typically integrate multiple objectives—such as clicks, watch time, likes, and follows—to perform a holistic ranking. However, effectively fusing these diverse tasks to reflect overall user satisfaction remains a formidable challenge. Existing approaches struggle with distributional discrepancies among objectives and rely on overly simplistic fusion mechanisms, resulting in degraded optimal performance. In this work, we reframe multi-objective ranking from a novel distribution-alignment perspective and propose a Structured Task Alignment Framework (STAF) to effectively model the user-satisfaction score. By mapping the distinct distributions of various tasks into a unified Gaussian space, the proposed method not only preserves crucial task-specific information but also ensures a smooth loss landscape and continuous gradients for more robust optimization. Furthermore, we address the subsequent challenge of integrating aforementioned user-satisfaction scores with a pre-existing, aggregated business-score (e.g., cold-start, e-commerce, advertising). We introduce a Sensitivity-Driven Fusion method, which flexibly utilizes the regional contribution of scores, thereby maximizing our user-satisfaction's impact without compromising established business metrics. Through extensive experiments on large-scale real-world datasets, we demonstrate that our approach consistently outperforms state-of-the-art multi-task baselines.