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

TL;DR

FedCRF is a privacy-preserving federated learning approach that leverages textual semantics and deep knowledge fusion to improve cross-domain recommendations without overlapping users or items.

Contribution

It introduces a novel semantic-driven federated recommendation framework that effectively transfers knowledge across domains using textual semantics and deep learning techniques.

Findings

FedCRF significantly outperforms existing methods in Recall@20 and NDCG@20.

The method effectively mitigates privacy risks by sharing only item semantic representations.

Experimental results validate the effectiveness of FedCRF in non-overlapping cross-domain scenarios.

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.