用于应用排序的残差多任务学习器
Residual Multi-Task Learner for Applied Ranking
AI总结：
针对电商排序中现有多任务学习方法的性能与适用性局限，本文提出轻量级框架ResFlow，通过任务网络间的残差连接实现高效跨任务信息共享，提升了排序性能并有效解决了在线与离线指标不一致问题。
摘要：
现代电子商务平台严重依赖对多样化用户反馈的建模来提供个性化服务。因此，多任务学习已成为其排序系统不可或缺的一部分。然而，现有的多任务学习方法面临两个主要挑战：一些方法缺乏对任务关系的显式建模，导致性能较差；而另一些方法由于计算量大、存在可扩展性问题或依赖强假设，导致适用性有限。为了解决这些局限性并更好地适应我们的真实场景——Shopee Search的预排序，我们在本文中引入了ResFlow，这是一个轻量级的多任务学习框架，通过任务网络对应层之间的残差连接实现高效的跨任务信息共享。在来自各种场景和模态的数据集上进行的大量实验表明，与最先进的方法相比，它具有卓越的性能和适应性。Shopee Search中的在线A/B测试展示了其在大规模工业应用中的实际价值，OPU（order-per-user，每用户订单量）增加了1.29%且无额外的系统延迟。ResFlow现已完全部署在Shopee Search的预排序模块中。为了促进高效的在线部署，我们提出了一种新颖的离线指标Weighted Recall@K，它与我们的在线指标OPU高度一致，解决了长期存在的在线与离线指标不一致问题。此外，我们提出在对项目进行排序时，将多个任务的分数进行加法融合，这优于传统的乘法融合。代码已发布在https://github.com/BrunoTruthAlliance/ResFlow
英文摘要：
Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their ranking systems. However, existing multi-task learning methods encounter two main challenges: some lack explicit modeling of task relationships, resulting in inferior performance, while others have limited applicability due to being computationally intensive, having scalability issues, or relying on strong assumptions. To address these limitations and better fit our real-world scenario, pre-rank in Shopee Search, we introduce in this paper ResFlow, a lightweight multi-task learning framework that enables efficient cross-task information sharing via residual connections between corresponding layers of task networks. Extensive experiments on datasets from various scenarios and modalities demonstrate its superior performance and adaptability over state-of-the-art methods. The online A/B tests in Shopee Search showcase its practical value in large-scale industrial applications, evidenced by a 1.29% increase in OPU (order-per-user) without additional system latency. ResFlow is now fully deployed in the pre-rank module of Shopee Search. To facilitate efficient online deployment, we propose a novel offline metric Weighted Recall@K, which aligns well with our online metric OPU, addressing the longstanding online-offline metric misalignment issue. Besides, we propose to fuse scores from the multiple tasks additively when ranking items, which outperforms traditional multiplicative fusion. The code is released at https://github.com/BrunoTruthAlliance/ResFlow
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。
