GradCraft：通过整体梯度构建提升多任务推荐
GradCraft: Elevating Multi-task Recommendations through Holistic Gradient Crafting
AI总结：
针对推荐系统中多任务学习的梯度平衡问题，提出 GradCraft 方法，通过动态调整梯度幅度和投影消除方向冲突，实现幅度与全局方向平衡，提升多任务推荐性能。
摘要：
推荐系统需要同时优化多个目标以准确建模用户兴趣，这 necessitates 多任务学习方法的应用。然而，现有的推荐多任务学习方法忽视了推荐场景的特定特征，在实现适当的梯度平衡方面存在不足。为应对这一挑战，我们将多任务学习的目标设定为实现适当的幅度平衡和全局方向平衡，并提出创新方法 GradCraft。GradCraft 动态调整梯度幅度以对齐最大梯度范数，减轻梯度幅度对后续操作的干扰。随后，它采用投影方法消除方向上的梯度冲突，同时考虑所有冲突任务，理论上保证全局解决方向冲突。GradCraft 确保同时实现适当的幅度平衡和全局方向平衡，符合推荐场景的固有特征。离线和在线实验均证实了 GradCraft 在提升推荐多任务性能方面的有效性。GradCraft 的源代码可在 https://github.com/baiyimeng/GradCraft 获取。
英文摘要：
Recommender systems require the simultaneous optimization of multiple objectives to accurately model user interests, necessitating the application of multi-task learning methods. However, existing multi-task learning methods in recommendations overlook the specific characteristics of recommendation scenarios, falling short in achieving proper gradient balance. To address this challenge, we set the target of multi-task learning as attaining the appropriate magnitude balance and the global direction balance, and propose an innovative methodology named GradCraft in response. GradCraft dynamically adjusts gradient magnitudes to align with the maximum gradient norm, mitigating interference from gradient magnitudes for subsequent manipulation. It then employs projections to eliminate gradient conflicts in directions while considering all conflicting tasks simultaneously, theoretically guaranteeing the global resolution of direction conflicts. GradCraft ensures the concurrent achievement of appropriate magnitude balance and global direction balance, aligning with the inherent characteristics of recommendation scenarios. Both offline and online experiments attest to the efficacy of GradCraft in enhancing multi-task performance in recommendations. The source code for GradCraft can be accessed at https://github.com/baiyimeng/GradCraft.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
