Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation

TL;DR

This paper introduces a novel wavelet-enhanced adaptive frequency filtering method that dynamically captures personalized, non-stationary, and short-term behavioral patterns in sequential recommendation tasks, outperforming existing static frequency domain approaches.

Contribution

It proposes a new adaptive frequency filter with wavelet feature enhancement, addressing limitations of static filters and improving modeling of non-stationary signals in sequential recommendation.

Findings

Outperforms existing methods on four benchmark datasets.

Effectively captures short-term fluctuations and non-stationary signals.

Enhances recommendation accuracy and efficiency in long sequences.

Abstract

Sequential recommendation has garnered significant attention for its ability to capture dynamic preferences by mining users' historical interaction data. Given that users' complex and intertwined periodic preferences are difficult to disentangle in the time domain, recent research is exploring frequency domain analysis to identify these hidden patterns. However, current frequency-domain-based methods suffer from two key limitations: (i) They primarily employ static filters with fixed characteristics, overlooking the personalized nature of behavioral patterns; (ii) While the global discrete Fourier transform excels at modeling long-range dependencies, it can blur non-stationary signals and short-term fluctuations. To overcome these limitations, we propose a novel method called Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation. Specifically, it consists of two vital modules: dynamic frequency-domain filtering and wavelet feature enhancement. The former is used to dynamically adjust filtering operations based on behavioral sequences to extract personalized global information, and the latter integrates wavelet transform to reconstruct sequences, enhancing blurred non-stationary signals and short-term fluctuations. Finally, these two modules work to achieve comprehensive performance and efficiency optimization in long sequential recommendation scenarios. Extensive experiments on four widely-used benchmark datasets demonstrate the superiority of our work.