FedCRF: A Federated Cross-domain Recommendation Method with Semantic-driven Deep Knowledge Fusion

2026-04-19

AI Summary

This work addresses the challenge of non-overlapping cross-domain recommendation, where neither shared users nor items exist and privacy preservation is paramount. To bridge the domains without direct user or item overlap, the authors propose a semantic-driven federated cross-domain recommendation framework that leverages textual semantics as a cross-domain connector. Within a federated learning paradigm, the server aggregates global semantic clusters, while each client employs a Federated Graph Semantic Adaptation Transformer (FGSAT) module to dynamically align with its local data distribution. The approach further integrates semantic graph neural networks with global-local contrastive learning to enhance representation consistency across domains. This method achieves privacy-preserving cross-domain knowledge transfer under strict non-overlap conditions, effectively mitigating inter-domain distribution shift. Extensive experiments on multiple real-world datasets demonstrate substantial improvements over state-of-the-art baselines, particularly in Recall@20 and NDCG@20 metrics.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

As user behavior data becomes increasingly scattered across different platforms, achieving cross-domain knowledge fusion while preserving privacy has become a critical issue in recommender systems. Existing PPCDR methods usually rely on overlapping users or items as a bridge, making them inapplicable to non-overlapping scenarios. They also suffer from limitations in the collaborative modeling of global and local semantics. To this end, this paper proposes a Federated Cross-domain Recommendation method with deep knowledge Fusion (FedCRF). Using textual semantics as a cross-domain bridge, FedCRF achieves cross-domain knowledge transfer via federated semantic learning under the non-overlapping scenario. Specifically, FedCRF constructs global semantic clusters on the server side to extract shared semantic information, and designs a FGSAT module on the client side to dynamically adapt to local data distributions and alleviate cross-domain distribution shift. Meanwhile, it builds a semantic graph based on textual features to learn representations that integrate both structural and semantic information, and introduces contrastive learning constraints between global and local semantic representations to enhance semantic consistency and promote deep knowledge fusion. In this framework, only item semantic representations are shared, while user interaction data remains locally stored, effectively mitigating privacy leakage risks. Experimental results on multiple real-world datasets show that FedCRF significantly outperforms existing methods in terms of Recall@20 and NDCG@20, validating its effectiveness and superiority in non-overlapping cross-domain recommendation scenarios.

Problem

• cross-domain recommendation
• privacy preservation
• non-overlapping domains
• semantic fusion
• federated learning

Innovation

• Federated Learning
• Cross-domain Recommendation
• Semantic Fusion
• Non-overlapping Domains
• Contrastive Learning