FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-domain behavioral data. While frequency-domain analysis offers a global perspective to address this, existing approaches typically treat user sequences in isolation, overlooking the crucial context of the target item. In this work, we present a novel empirical observation: user attention scores exhibit distinct spectral entropy distributions when conditioned on positive versus negative target items. Specifically, true user interests manifest as highly concentrated spectral patterns with lower entropy in the frequency domain, whereas irrelevant ...
| Authors | Zenan Dai, Jinpeng Wang, Junwei Pan et al. |
|---|---|
| Primary category | cs.IR |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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