HiWaveRec: Hierarchical Wavelet Decoupling Framework with Adaptive Gating for Sequential Recommendation

Sequential recommendation predicts users’ next interactions by modeling the dynamic evolution of their interests from historical behavior sequences. Given that long-term and short-term interests are intrinsically intertwined within these sequences, existing methods struggle to capture this complexity accurately, facing two key challenges: (i) inadequate decoupling, which causes dominant long-term interests to overshadow localized short-term interests; and (ii) uniform processing strategies, which ignore the distinct modeling requirements of long-term and short-term interests, failing to distinguish informative short-term interests from incidental noise. To address these challenges, we propose a new framework, Hierarchical Wavelet Decoupling Framework with Adaptive Gating for Sequential Recommendation (HiWaveRec), which enables differentiated modeling of long-term and short-term interests. Specifically, HiWaveRec applies a multi-level Discrete Wavelet Transform (DWT) to decouple interaction sequences into low-frequency long-term interests and multi-scale high-frequency short-term interests while preserving temporal locality. It then employs a heterogeneous dual-branch architecture, where Low-Frequency Self-Attention (LFSA) captures global dependencies in long-term interests and Causal Detail Gating (CDG) selectively amplifies informative short-term interests while suppressing noise. Finally, the processed sub-bands are fused through Inverse DWT(IDWT) to reconstruct a unified sequence representation. Experiments on four benchmark datasets demonstrate that HiWaveRec achieves competitive or superior performance compared with strong baselines. The source code is available here ( https://github.com/coderxxw/HiWaveRec ).