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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Pith papers citing it
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cs.IR 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
A framework integrates MM-LLMs into recommendation systems via caption generation as categorical features, reporting 0.35% offline AUC lift and 0.02% online metric improvement.
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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Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
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A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems
A framework integrates MM-LLMs into recommendation systems via caption generation as categorical features, reporting 0.35% offline AUC lift and 0.02% online metric improvement.