SemaCDR builds a unified semantic space with LLM-generated domain-agnostic features and adaptive fusion to improve cross-domain sequential recommendations over baselines.
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
CoDiS applies variational context adjustment, expert isolation, and adversarial disentanglement to separate domain-shared and domain-specific preferences in cross-domain sequential recommendation, outperforming baselines on three datasets.
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SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation
SemaCDR builds a unified semantic space with LLM-generated domain-agnostic features and adaptive fusion to improve cross-domain sequential recommendations over baselines.
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Context-Aware Disentanglement for Cross-Domain Sequential Recommendation: A Causal View
CoDiS applies variational context adjustment, expert isolation, and adversarial disentanglement to separate domain-shared and domain-specific preferences in cross-domain sequential recommendation, outperforming baselines on three datasets.