FEDIN improves CTR prediction by using target-aware frequency filtering to isolate low-entropy periodic interest signals from high-entropy noise in user attention patterns.
InProceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.IR 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
WPGRec is a new sequential recommender that performs multi-scale temporal modeling via stationary wavelet packets and injects high-order collaborative information through scale-aligned graph propagation with energy-aware gated fusion.
citing papers explorer
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FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
FEDIN improves CTR prediction by using target-aware frequency filtering to isolate low-entropy periodic interest signals from high-entropy noise in user attention patterns.
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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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WPGRec: Wavelet Packet Guided Graph Enhanced Sequential Recommendation
WPGRec is a new sequential recommender that performs multi-scale temporal modeling via stationary wavelet packets and injects high-order collaborative information through scale-aligned graph propagation with energy-aware gated fusion.