  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.
