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This paper proposes a personalized Federated Mask-based Selective PEFT framework, which significantly outperforms the existing federated PEFT method on both seen and unseen classes, and achieves balanced performance on all clients.
This work presents UniGCRec, which constructs a cross-domain user profile from multi-domain histories and quantizes both users and items into CSC-IDs that integrate semantic and collaborative signals, effectively mitigating user-item asymmetry and enabling preference-aware selective transfer under low-overlap settings.
By pre-training artificial neural networks exclusively on large-scale synthetic data, this work demonstrates robust zero-shot generalization across diverse brain regions, experimental paradigms and species, enabling the accurate inference of single-unit activities and cell-type properties without exposure to real data.
Cross-domain sequential recommendation (CDSR) improves target-domain prediction by leveraging multi-domain interaction histories. Most CDSR methods rely on shared entities or co-occurrence signals, which become unreliable when overlap is limited, and atomic ID representations further generalize poorly to long-tail or u...
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