Recommendation systems effectively guide users in locating their desired information within extensive content repositories. Generally, a recommendation model is optimized to enhance accuracy metrics from a user utility standpoint, such as click-through rate or matching relevance. However, a responsible industrial recommendation system must address not only user utility (responsibility to users) but also other objectives, including increasing platform revenue (responsibility to platforms), ensuring fairness (responsibility to content creators), and maintaining unbiasedness (responsibility to long-term healthy development). Multi-objective learning is a potent approach for achieving responsible recommendation systems. Nevertheless, current methods encounter two challenges: difficulty in scaling to heterogeneous objectives within a unified framework, and inadequate controllability over objective priority during optimization, leading to uncontrollable solutions. In this paper, we present a data-centric optimization framework, MoRec, which unifies the learning of diverse objectives. MoRec is a tri-level framework: the outer level manages the balance between different objectives, utilizing a proportional-integral-derivative (PID)-based controller to ensure a preset regularization on the primary objective. The middle level transforms objective-aware optimization into data sampling weights using sign gradients. The inner level employs a standard optimizer to update model parameters with the sampled data. Consequently, MoRec can flexibly support various objectives while maintaining the original model intact. Comprehensive experiments on two public datasets and one industrial dataset showcase the effectiveness, controllability, flexibility, and Pareto efficiency of MoRec, making it highly suitable for real-world implementation.

翻译：推荐系统有效地引导用户在庞大的内容库中找到其所需信息。通常，推荐模型会从用户效用角度优化准确性指标，例如点击率或匹配相关性。然而，负责任的工业推荐系统不仅需要考虑用户效用（对用户负责），还需兼顾其他目标，包括增加平台收益（对平台负责）、确保公平性（对内容创作者负责）以及维持无偏性（对长期健康发展负责）。多目标学习是实现负责任推荐系统的有效途径。然而，现有方法面临两个挑战：难以在统一框架中扩展至异质目标，且优化过程中对目标优先级控制不足，导致解不可控。本文提出了一种数据驱动优化框架MoRec，可统一学习多种目标。MoRec是一个三层框架：外层负责管理不同目标间的平衡，利用基于比例-积分-微分（PID）的控制器确保对主目标的预设正则化；中层通过符号梯度将目标感知优化转化为数据采样权重；内层使用标准优化器对采样数据更新模型参数。因此，MoRec能在保持原始模型不变的前提下，灵活支持多种目标。在两个公开数据集和一个工业数据集上的全面实验展示了MoRec的有效性、可控性、灵活性和帕累托效率，使其高度适用于实际部署。