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

arXiv: https://arxiv.org/abs/2409.01192 · HTML: https://mlsanity.com/paper/2409.01192/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2024.

## Built on by (most cited first, 9 of 9)

- [Diffusion Generative Recommendation with Continuous Tokens](https://mlsanity.com/paper/2504.12007/) (2025)
- [Computational Protein Science in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2501.10282/) (2025)
- [Continuous-time Discrete-space Diffusion Model for Recommendation](https://mlsanity.com/paper/2511.12114/) (2025)
- [SS4Rec: Continuous-Time Sequential Recommendation with State Space Models](https://mlsanity.com/paper/2502.08132/) (2025)
- [Damba-ST: Domain-Adaptive Mamba for Efficient Urban Spatio-Temporal Prediction](https://mlsanity.com/paper/2506.18939/) (2025)
- [DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis](https://mlsanity.com/paper/2601.05527/) (2026)
- [Sensory-Aware Sequential Recommendation via Review-Distilled Representations](https://mlsanity.com/paper/2603.02709/) (2026)
- [Dual-Interest Sequential Product Recommendation With Multi-Granular SSM](https://mlsanity.com/paper/2609.21548/) (2026)
- [ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents](https://mlsanity.com/paper/2609.37311/) (2026)

## Abstract (arXiv)

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 sequential recommendation backbone, SSD4Rec, which explores the seamless adaptation of Mamba for sequential recommendations. Specifically, SSD4Rec marks the variable- and long-length item sequences with sequence registers and processes the item representations with bidirectional Structured State Space Duality (SSD) blocks. 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 code is publicly available at https://github.com/ZhangYifeng1995/SSD4Rec.