TimeMM proposes a time-as-operator spectral filtering framework with adaptive mixing and modality routing to model non-stationary multimodal user preferences in recommendation systems.
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SG-URInit builds semantically enriched initial user representations for multimodal recommenders by fusing local item modality features with global cluster semantics, closing the gap with item representations without extra training.
citing papers explorer
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TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
TimeMM proposes a time-as-operator spectral filtering framework with adaptive mixing and modality routing to model non-stationary multimodal user preferences in recommendation systems.
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Well Begun is Half Done: Training-Free and Model-Agnostic Semantically Guaranteed User Representation Initialization for Multimodal Recommendation
SG-URInit builds semantically enriched initial user representations for multimodal recommenders by fusing local item modality features with global cluster semantics, closing the gap with item representations without extra training.