From Transfer to Collaboration: A Federated Framework for Cross-Market Sequential Recommendation

2026-04-15

AI Summary

This work addresses performance degradation and negative transfer in cross-market recommendation caused by data isolation, non-overlapping users, and market heterogeneity. To tackle these challenges, the authors propose FeCoSR, a novel framework that introduces a many-to-many federated collaboration paradigm. FeCoSR leverages federated pre-training to uncover shared behavioral patterns across markets and employs local fine-tuning to capture market-specific preferences. It further mitigates semantic discrepancies between markets through a Semantic Soft Cross-Entropy (S²CE) loss and enhances local recommendation capability via a market-adaptive module. Extensive experiments on real-world datasets demonstrate that FeCoSR significantly outperforms existing methods, effectively suppressing negative transfer and improving cross-market recommendation performance.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Data Mining & Knowledge Management: Recommender Systems
• Multiagent Systems: Coordination and Collaboration

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

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.

Problem

• cross-market recommendation
• source degradation
• negative transfer
• market heterogeneity
• data isolation

Innovation

• federated collaboration
• cross-market recommendation
• semantic soft cross-entropy
• many-to-many paradigm
• market heterogeneity