  Predicting user preferences and sequential dependencies based on historical
behavior is the core goal of sequential recommendation. Although
attention-based models have shown effectiveness in this field, they often
struggle with inference inefficiency due to the quadratic computational
complexity inherent in attention mechanisms, especially with long-range
behavior sequences. Drawing inspiration from the recent advancements of state
space models (SSMs) in control theory, which provide a robust framework for
modeling and controlling dynamic systems, we introduce EchoMamba4Rec. Control
theory emphasizes the use of SSMs for managing long-range dependencies and
maintaining inferential efficiency through structured state matrices.
EchoMamba4Rec leverages these control relationships in sequential
recommendation and integrates bi-directional processing with frequency-domain
filtering to capture complex patterns and dependencies in user interaction data
more effectively. Our model benefits from the ability of state space models
(SSMs) to learn and perform parallel computations, significantly enhancing
computational efficiency and scalability. It features a bi-directional Mamba
module that incorporates both forward and reverse Mamba components, leveraging
information from both past and future interactions. Additionally, a filter
layer operates in the frequency domain using learnable Fast Fourier Transform
(FFT) and learnable filters, followed by an inverse FFT to refine item
embeddings and reduce noise. We also integrate Gate Linear Units (GLU) to
dynamically control information flow, enhancing the model's expressiveness and
training stability. Experimental results demonstrate that EchoMamba
significantly outperforms existing models, providing more accurate and
personalized recommendations.
