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Beijing Jiaotong University
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FedCGR revolutionizes cross-domain recommendations by enabling effective item alignment without compromising user privacy or requiring extensive data sharing.
Achieving up to 21.58% equal error rates, this method outperforms traditional approaches by effectively disentangling speaker identity from synthesis artifacts.
