WPGRec: Wavelet Packet Guided Graph Enhanced Sequential Recommendation

• URL: http://arxiv.org/abs/2604.21305v1
• Date: Thu, 23 Apr 2026 05:44:26 GMT
• Title: WPGRec: Wavelet Packet Guided Graph Enhanced Sequential Recommendation
• Abstract summary: Sequential recommendation aims to model users' evolving interests from noisy and non-stationary interaction streams.<n>We propose Wavelet Packet Guided Graph Enhanced Sequential Recommendation (WPGRec)<n>WPGRec applies a full-tree undecimated stationary wavelet packet transform to generate equal-length, shift-invariant subband sequences.<n>It then performs subband-wise interaction-graph propagation to inject high-order collaborative information.
• Score: 9.195401107720498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential recommendation aims to model users' evolving interests from noisy and non-stationary interaction streams, where long-term preferences, short-term intents, and localized behavioral fluctuations may coexist across temporal scales. Existing frequency-domain methods mainly rely on either global spectral operations or filter-based wavelet processing. However, global spectral operations tend to entangle local transients with long-range dependencies, while filter-based wavelet pipelines may suffer from temporal misalignment and boundary artifacts during multi-scale decomposition and reconstruction. Moreover, collaborative signals from the user-item interaction graph are often injected through scale-inconsistent auxiliary modules, limiting the benefit of jointly modeling temporal dynamics and structural dependencies. To address these issues, we propose Wavelet Packet Guided Graph Enhanced Sequential Recommendation (WPGRec), a unified time-frequency and graph-enhanced framework that aligns multi-resolution temporal modeling with graph propagation at matching scales. WPGRec first applies a full-tree undecimated stationary wavelet packet transform to generate equal-length, shift-invariant subband sequences. It then performs subband-wise interaction-graph propagation to inject high-order collaborative information while preserving temporal alignment across resolutions. Finally, an energy- and spectral-flatness-aware gated fusion module adaptively aggregates informative subbands and suppresses noise-like components. Extensive experiments on four public benchmarks show that WPGRec consistently outperforms sequential and graph-based baselines, with particularly clear gains on sparse and behaviorally complex datasets, highlighting the effectiveness of band-consistent structure injection and adaptive subband fusion for sequential recommendation.

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