WPGRec: Wavelet Packet Guided Graph Enhanced Sequential Recommendation

TL;DR

WPGRec introduces a unified wavelet packet and graph-based framework for sequential recommendation, effectively modeling multi-scale temporal dynamics and structural dependencies to improve recommendation accuracy.

Contribution

The paper proposes WPGRec, a novel method combining wavelet packet transform and graph propagation for better multi-scale temporal and structural modeling in recommendation systems.

Findings

WPGRec outperforms existing methods on four public benchmarks.

Significant improvements are observed on sparse and complex datasets.

Adaptive subband fusion effectively suppresses noise and enhances signals.

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.