In modern recommender systems, especially in e-commerce, predicting multiple targets such as click-through rate (CTR) and post-view conversion rate (CTCVR) is common. Multi-task recommender systems are increasingly popular in both research and practice, as they leverage shared knowledge across diverse business scenarios to enhance performance. However, emerging real-world scenarios and data privacy concerns complicate the development of a unified multi-task recommendation model. In this paper, we propose PF-MSMTrec, a novel framework for personalized federated multi-scenario multi-task recommendation. In this framework, each scenario is assigned to a dedicated client utilizing the Multi-gate Mixture-of-Experts (MMoE) structure. To address the unique challenges of multiple optimization conflicts, we introduce a bottom-up joint learning mechanism. First, we design a parameter template to decouple the expert network parameters, distinguishing scenario-specific parameters as shared knowledge for federated parameter aggregation. Second, we implement personalized federated learning for each expert network during a federated communication round, using three modules: federated batch normalization, conflict coordination, and personalized aggregation. Finally, we conduct an additional round of personalized federated parameter aggregation on the task tower network to obtain prediction results for multiple tasks. Extensive experiments on two public datasets demonstrate that our proposed method outperforms state-of-the-art approaches. The source code and datasets will be released as open-source for public access.

翻译：在现代推荐系统中，尤其是在电子商务领域，预测点击率（CTR）和浏览后转化率（CTCVR）等多个目标已成为常见需求。多任务推荐系统在研究和实践中日益普及，因为它们能够利用不同业务场景间的共享知识来提升性能。然而，新兴的现实场景和数据隐私问题使得开发统一的多任务推荐模型变得复杂。本文提出PF-MSMTrec，一种新颖的个性化联邦多场景多任务推荐框架。在该框架中，每个场景被分配给一个采用多门混合专家（MMoE）结构的专用客户端。为应对多重优化冲突带来的独特挑战，我们引入了一种自底向上的联合学习机制。首先，我们设计了一个参数模板来解耦专家网络参数，将场景特定参数区分为联邦参数聚合的共享知识。其次，我们在联邦通信轮次中为每个专家网络实施个性化联邦学习，使用三个模块：联邦批量归一化、冲突协调和个性化聚合。最后，我们在任务塔网络上进行额外一轮的个性化联邦参数聚合，以获得多任务的预测结果。在两个公开数据集上的大量实验表明，我们提出的方法优于现有最先进方法。源代码和数据集将作为开源资源公开发布。