# Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models

arXiv: https://arxiv.org/abs/2403.03900 · HTML: https://mlsanity.com/paper/2403.03900/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2024.

## Built on by (most cited first, 26 of 26)

- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity](https://mlsanity.com/paper/2412.06148/) (2024)
- [Reasoning Beyond Limits: Advances and Open Problems for LLMs](https://mlsanity.com/paper/2503.22732/) (2025)
- [Mamba4KT:An Efficient and Effective Mamba-based Knowledge Tracing Model](https://mlsanity.com/paper/2405.16542/) (2024)
- [FTMixer: Frequency and Time Domain Representations Fusion for Time Series Modeling](https://mlsanity.com/paper/2405.15256/) (2024)
- [Future-Aware Interaction Network For Motion Forecasting](https://mlsanity.com/paper/2503.06565/) (2025)
- [Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation](https://mlsanity.com/paper/2604.20858/) (2026)
- [SS4Rec: Continuous-Time Sequential Recommendation with State Space Models](https://mlsanity.com/paper/2502.08132/) (2025)
- [Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models](https://mlsanity.com/paper/2503.11224/) (2025)
- [Who You Are Matters: Bridging Topics and Social Roles via LLM-Enhanced Logical Recommendation](https://mlsanity.com/paper/2505.10940/) (2025)
- [Venturing into Uncharted Waters: The Navigation Compass from Transformer to Mamba](https://mlsanity.com/paper/2406.16722/) (2024)
- [Mamba for Scalable and Efficient Personalized Recommendations](https://mlsanity.com/paper/2409.17165/) (2024)
- [Recommendation Retrievers Need Verifiers: Universal Generative Reranking for Sequential Recommendations](https://mlsanity.com/paper/2609.12270/) (2026)
- [Exploring Applications of State Space Models and Advanced Training Techniques in Sequential Recommendations: A Comparative Study on Efficiency and Performance](https://mlsanity.com/paper/2408.05606/) (2024)
- [FSMLP: Modelling Channel Dependencies With Simplex Theory Based Multi-Layer Perceptions In Frequency Domain](https://mlsanity.com/paper/2412.01654/) (2024)
- [ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibility Data](https://mlsanity.com/paper/2505.12638/) (2025)
- [TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation](https://mlsanity.com/paper/2510.25259/) (2025)
- [Bridging Textual Profiles and Latent User Embeddings for Personalization](https://mlsanity.com/paper/2605.06981/) (2026)
- [Graph Mamba Survival Analysis Based on Topology-Aware ordering](https://mlsanity.com/paper/2606.02602/) (2026)
- [SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks](https://mlsanity.com/paper/2606.09888/) (2026)
- [State Space Models Meet Remote Sensing: A Survey](https://mlsanity.com/paper/2606.25329/) (2026)
- [GRAIN: Molecules Are Not the Right Granularity -- Active-Ingredient Modeling for Safe Medication Recommendation](https://mlsanity.com/paper/2608.00098/) (2026)
- [Dual-Interest Sequential Product Recommendation With Multi-Granular SSM](https://mlsanity.com/paper/2609.21548/) (2026)
- [Not All Is Lost: Repairing Lossy User Preference States of Personalization Encoders](https://mlsanity.com/paper/2610.01270/) (2026)
- [Action-On-Item Preference Flow: A Shared Event Schema for Predictive and Generative Personalization](https://mlsanity.com/paper/2610.01375/) (2026)
- [MARS: Multi-resolution Adaptive Routing for Sequential Recommendation](https://mlsanity.com/paper/2610.07505/) (2026)

## Abstract (arXiv)

Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven to be effective for sequential recommendation, they suffer from the inference inefficiency problem stemming from the quadratic computational complexity of attention operators, especially for long behavior sequences. Inspired by the recent success of state space models (SSMs), we propose Mamba4Rec, which is the first work to explore the potential of selective SSMs for efficient sequential recommendation. Built upon the basic Mamba block which is a selective SSM with an efficient hardware-aware parallel algorithm, we design a series of sequential modeling techniques to further promote model performance while maintaining inference efficiency. Through experiments on public datasets, we demonstrate how Mamba4Rec effectively tackles the effectiveness-efficiency dilemma, outperforming both RNN- and attention-based baselines in terms of both effectiveness and efficiency. The code is available at https://github.com/chengkai-liu/Mamba4Rec.