Balanced Frequency Decoupling: Energy-Aware Multi-Scale Preference Modeling for Sequential Recommendation

Frequency-domain sequential recommendation models enhance sequence representation capacity through spectral transformations. However, existing methods typically adopt coarse-grained spectrum reweighting strategies that strengthen high-frequency components while amplifying random noise within frequency bands. Moreover, they generally rely on a coupled modeling mechanism that handles both long-term and short-term preferences within a single backbone network, lacking dedicated modeling paths tailored to their distinct temporal characteristics. To address these challenges, we propose a Balanced Frequency Decoupling Sequential Recommendation model (BFDRec). Specifically, we design an energy-aware spectrum denoising mechanism to adaptively suppress low-energy noises according to the energy distribution within each frequency band while preserving salient behavioral fluctuation signals. Additionally, we construct a multi-scale decoupled architecture to model users' multi-scale preferences and adaptively integrate them through a dynamic gating mechanism, aligning the sequence modeling process with the distinct temporal characteristics of different frequency bands. Extensive experiments across five real-world datasets demonstrate that BFDRec effectively achieves noise suppression and accurate multi-scale preference modeling, with average improvements of 6.67% and 5.05% in HR and NDCG, respectively, over advanced baseline models. Our code is available at https://anonymous.4open.science/r/BFDRec.