SSD4Rec: A Structured State Space Duality Model for Efficient Sequential Recommendation

Sequential recommendation methods are crucial in modern recommender systems for their remarkable capability to understand a user’s changing interests based on past interactions. However, a significant challenge faced by current methods (e.g., RNN- or Transformer-based models) is to effectively and efficiently capture users’ preferences by modeling long behavior sequences, which impedes their various applications like short video platforms where user interactions are numerous. Recently, an emerging architecture named Mamba , built on state space models (SSM) with efficient hardware-aware designs, has showcased the tremendous potential for sequence modeling, presenting a compelling avenue for addressing the challenge effectively. Inspired by this, we propose a novel generic and efficient framework ( SSD4Rec ) for sequential recommendations, which explores the seamless adaptation of Mamba for recommendations. Specifically, SSD4Rec marks the long-length item sequences with sequence registers and processes the item representations with a novel Masked Bidirectional Structured State Space Duality block. This not only allows for hardware-aware matrix multiplication but also empowers outstanding capabilities in variable-length and long-range sequence modeling. Extensive evaluations on four benchmark datasets demonstrate that the proposed model achieves state-of-the-art performance while maintaining near-linear scalability with user sequence length. Our implementation based on PyTorch is available at https://github.com/ZhangYifeng1995/SSD4Rec .