# SIGMA: Selective Gated Mamba for Sequential Recommendation arXiv: https://arxiv.org/abs/2408.11451 ยท HTML: https://mlsanity.com/paper/2408.11451/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Placed in Core SSM architectures for language, one of 10 directions in State-space sequence models (S4, Mamba). - Builds on 26 of its 43 references that are in the map. - First posted on arXiv 21 Aug 2024. ## Placed in - State-space sequence models (S4, Mamba) / [Core SSM architectures for language](https://mlsanity.com/ssm/core-ssm-architectures-for-language/) ## Builds on (most cited first, 26 of 26) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018) - [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018) - [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019) - [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022) - [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015) - [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017) - [On The Computational Complexity of Self-Attention](https://mlsanity.com/paper/2209.04881/) (2022) - [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018) - [A Simple Convolutional Generative Network for Next Item Recommendation](https://mlsanity.com/paper/1808.05163/) (2018) - [Sequential Recommender Systems: Challenges, Progress and Prospects](https://mlsanity.com/paper/2001.04830/) (2019) - [Hierarchically Gated Recurrent Neural Network for Sequence Modeling](https://mlsanity.com/paper/2311.04823/) (2023) - [Frequency Enhanced Hybrid Attention Network for Sequential Recommendation](https://mlsanity.com/paper/2304.09184/) (2023) - [Multi-Task Deep Recommender Systems: A Survey](https://mlsanity.com/paper/2302.03525/) (2023) - [KuaiSim: A Comprehensive Simulator for Recommender Systems](https://mlsanity.com/paper/2309.12645/) (2023) - [Rethinking Large Language Model Architectures for Sequential Recommendations](https://mlsanity.com/paper/2402.09543/) (2024) - [Multi-Task Recommendations with Reinforcement Learning](https://mlsanity.com/paper/2302.03328/) (2023) - [AutoMLP: Automated MLP for Sequential Recommendations](https://mlsanity.com/paper/2303.06337/) (2023) - [AutoDenoise: Automatic Data Instance Denoising for Recommendations](https://mlsanity.com/paper/2303.06611/) (2023) - [Bidirectional Distillation for Top-K Recommender System](https://mlsanity.com/paper/2106.02870/) (2021) - [Denoising and Prompt-Tuning for Multi-Behavior Recommendation](https://mlsanity.com/paper/2302.05862/) (2023) - [Exploration and Regularization of the Latent Action Space in Recommendation](https://mlsanity.com/paper/2302.03431/) (2023) - [Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation](https://mlsanity.com/paper/2403.16371/) (2024) - [EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential Recommendation](https://mlsanity.com/paper/2406.02638/) (2024) ## Built on by (most cited first, 10 of 10) - [Block-Biased Mamba for Long-Range Sequence Processing](https://mlsanity.com/paper/2505.09022/) (2025) - [Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs](https://mlsanity.com/paper/2509.02017/) (2025) - [LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training](https://mlsanity.com/paper/2511.19931/) (2025) - [AF-MAT: Aspect-aware Flip-and-Fuse xLSTM for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2507.01213/) (2025) - [Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representations](https://mlsanity.com/paper/2508.20945/) (2025) - [The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation](https://mlsanity.com/paper/2512.10388/) (2025) - [U-MASK: User-adaptive Spatio-Temporal Masking for Personalized Mobile AI Applications](https://mlsanity.com/paper/2601.06867/) (2026) - [Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding](https://mlsanity.com/paper/2607.28659/) (2026) - [Dual-Interest Sequential Product Recommendation With Multi-Granular SSM](https://mlsanity.com/paper/2609.21548/) (2026) - [RouteRec: Behavior-Guided Sparse Routing for Sequential Recommendation](https://mlsanity.com/paper/2609.39007/) (2026) ## Abstract (arXiv) In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences. Typically, SRS utilize transformer-based architectures to forecast the subsequent item within a sequence. Nevertheless, the quadratic computational complexity inherent in these models often leads to inefficiencies, hindering the achievement of real-time recommendations. Mamba, a recent advancement, has exhibited exceptional performance in time series prediction, significantly enhancing both efficiency and accuracy. However, integrating Mamba directly into SRS poses several challenges. Its inherently unidirectional nature may constrain the model's capacity to capture the full context of user-item interactions, while its instability in state estimation can compromise its ability to detect short-term patterns within interaction sequences. To overcome these issues, we introduce a new framework named Selective Gated Mamba (SIGMA) for Sequential Recommendation. This framework leverages a Partially Flipped Mamba (PF-Mamba) to construct a bidirectional architecture specifically tailored to improve contextual modeling. Additionally, an input-sensitive Dense Selective Gate (DS Gate) is employed to optimize directional weights and enhance the processing of sequential information in PF-Mamba. For short sequence modeling, we have also developed a Feature Extract GRU (FE-GRU) to efficiently capture short-term dependencies. Empirical results indicate that SIGMA outperforms current models on five real-world datasets. Our implementation code is available at https://github.com/ziwliu-cityu/SIMGA to ease reproducibility.