Semantic and collaborative representations show low item-level overlap on sparse data, so global alignment suppresses complementary signals and a shared-plus-private fusion design is needed instead.
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DIAURec unifies intent and language modeling to reconstruct and optimize representations in prototype and distribution spaces, outperforming baselines on three datasets.
HaNoRec dynamically weights harder preference samples and applies Gaussian perturbations to output distributions to improve multimodal LLM performance on sequential recommendation tasks.
citing papers explorer
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Rethinking Semantic Collaborative Integration: Why Alignment Is Not Enough
Semantic and collaborative representations show low item-level overlap on sparse data, so global alignment suppresses complementary signals and a shared-plus-private fusion design is needed instead.
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DIAURec: Dual-Intent Space Representation Optimization for Recommendation
DIAURec unifies intent and language modeling to reconstruct and optimize representations in prototype and distribution spaces, outperforming baselines on three datasets.
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Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation
HaNoRec dynamically weights harder preference samples and applies Gaussian perturbations to output distributions to improve multimodal LLM performance on sequential recommendation tasks.