FeCoSR replaces one-to-one transfer with federated pretraining using Semantic Soft Cross-Entropy and local fine-tuning to avoid source degradation and negative transfer in cross-market sequential recommendation.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
CAST improves sequential recommendation by modeling fine-grained semantic transitions and using LLM priors to capture true item complementarity, reporting up to 17.6% Recall and 16.0% NDCG gains over prior methods.
citing papers explorer
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From Transfer to Collaboration: A Federated Framework for Cross-Market Sequential Recommendation
FeCoSR replaces one-to-one transfer with federated pretraining using Semantic Soft Cross-Entropy and local fine-tuning to avoid source degradation and negative transfer in cross-market sequential recommendation.
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CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation
CAST improves sequential recommendation by modeling fine-grained semantic transitions and using LLM priors to capture true item complementarity, reporting up to 17.6% Recall and 16.0% NDCG gains over prior methods.