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

A research team led by Jundong Chen has introduced FeCoSR (Federated Collaboration for Sequential Recommendation), a novel framework that fundamentally changes how AI models handle recommendations across different geographic markets. Traditional cross-market recommendation systems have struggled with two major challenges: source degradation (where improving one market hurts another) and negative transfer (where market differences cause poor performance). FeCoSR replaces the conventional one-to-one transfer paradigm with a many-to-many collaboration approach, allowing all markets to jointly participate in training while maintaining data isolation.

The framework operates in two key stages: federated pretraining captures shared behavioral patterns across markets without sharing raw data, followed by local fine-tuning that adapts to specific market preferences. To address market heterogeneity, the researchers developed Semantic Soft Cross-Entropy (S²CE), a novel loss function that leverages semantic information to facilitate better collaborative learning. This approach prevents the model from being biased toward any single market's data distribution, which has been a persistent problem in global recommendation systems.

Extensive testing on real-world datasets demonstrates that FeCoSR outperforms existing methods by approximately 15% in recommendation accuracy while completely preserving data privacy. The system enables companies like Amazon, Netflix, or Spotify to build unified recommendation engines that work across different countries without violating data sovereignty laws or compromising user privacy. This represents a significant advancement in federated learning applications for e-commerce and content platforms operating in multiple regulatory environments.