Cross-market recommendation (CMR) aims to enhance recommendation performance across multiple markets. Due to its inherent characteristics, i.e., data isolation, non-overlapping users, and market heterogeneity, CMR introduces unique challenges and fundamentally differs from cross-domain recommendation (CDR). Existing CMR approaches largely inherit CDR by adopting the one-to-one transfer paradigm, where a model is pretrained on a source market and then fine-tuned on a target market. However, such a paradigm suffers from CH1. source degradation, where the source market sacrifices its own performance for the target markets, and CH2. negative transfer, where market heterogeneity leads to suboptimal performance in target markets. To address these challenges, we propose FeCoSR, a novel federated collaboration framework for cross-market sequential recommendation. Specifically, to tackle CH1, we introduce a many-to-many collaboration paradigm that enables all markets to jointly participate in and benefit from training. It consists of a federated pretraining stage for capturing shared behavior-level patterns, followed by local fine-tuning for market-specific item-level preferences. For CH2, we theoretically and empirically show that vanilla Cross-Entropy (CE) exacerbates market heterogeneity, undermining federated optimization. To address this, we propose a Semantic Soft Cross-Entropy (S^2CE) that leverages shared semantic information to facilitate collaborative behavioral learning across markets. Then, we design a market-specific adaptation module during fine-tuning to capture local item preferences. Extensive experiments on the real-world datasets demonstrate the advantages of FeCoSR over other methods.

翻译：跨市场推荐（Cross-Market Recommendation, CMR）旨在提升多个市场间的推荐性能。由于其固有特性，即数据隔离、用户无重叠以及市场异质性，CMR引入了独特的挑战，并从根本上区别于跨域推荐（Cross-Domain Recommendation, CDR）。现有CMR方法主要通过采用一对一的迁移范式来继承CDR，即在一个源市场上预训练模型，随后在目标市场上进行微调。然而，这种范式存在两大问题：挑战一（CH1）源市场性能下降，即源市场为提升目标市场性能而牺牲自身表现；挑战二（CH2）负迁移，即市场异质性导致目标市场性能次优。为解决这些挑战，我们提出了FeCoSR——一种用于跨市场序列推荐的新型联邦协作框架。具体而言，针对CH1，我们引入一种多对多的协作范式，使所有市场能够共同参与训练并从中受益。该范式包含一个联邦预训练阶段，用于捕获共享的行为级模式，随后进行针对市场特定项目级偏好的本地微调。针对CH2，我们从理论和实证上证明：标准的交叉熵损失函数（Cross-Entropy, CE）会加剧市场异质性，从而损害联邦优化效果。为此，我们提出一种语义软交叉熵损失函数（Semantic Soft Cross-Entropy, S^2CE），利用共享的语义信息促进跨市场的协作行为学习。然后，我们在微调阶段设计了一个市场特定的自适应模块，以捕获本地项目偏好。在真实世界数据集上的广泛实验证明了FeCoSR相对于其他方法的优势。