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.
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
School of Artificial Intelligence, Jilin University, Changchun, China（吉林大学人工智能学院）；Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China（香港科技大学（广州））；College of Computer Science and Technology, Jilin University, Changchun, China（吉林大学计算机科学与技术学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China（知识驱动的人机智能工程研究中心，教育部）
DA-Mamba: Dialogue-aware selective state-space model for multimodal engagement estimation
Communication University of China（中国通信大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院）；School of Information Engineering, Beijing Institute of Graphic Communication（北京印刷学院信息工程学院）
Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation
Unit of Artificial Intelligence and Computer Systems, Università Campus Bio-Medico di Roma, Italy（人工智能与计算机系统单位，罗马大学生物医学校园，意大利）；College of Computer Science and Software Engineering, Shenzhen University, China（计算机科学与软件工程学院，深圳大学，中国）；Dept. of Computer Engineering, Modeling, Electronic and System Engineering, University of Calabria, Italy（计算机工程、建模、电子与系统工程系，卡拉布里亚大学，意大利）；IRCCS San Raffaele Hospital, Italy（圣拉斐拉医院，意大利）；Faculty of Medicine, Vita-Salute San Raffaele University, Italy（医学学院，维塔-桑拉斐拉大学，意大利）；Dept. of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Sweden（诊断与干预系，辐射物理，生物医学工程，乌梅大学，瑞典）；Dept. of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Italy（海军、电子、电子与电信工程系，热那亚大学，意大利）
Repetitive Contrastive Learning Enhances Mamba's Selectivity in Time Series Prediction
School of Intelligence Science and Technology, Peking University, Beijing（智能科学与技术学院，北京大学，北京）；Computational Intelligence Laboratory（计算智能实验室）；Institute for Artificial Intelligence（人工智能研究院）；State Key Laboratory of General Artificial Intelligence, Peking University, Beijing（通用人工智能国家重点实验室，北京大学，北京）
MambaLRP: Explaining Selective State Space Sequence Models
Technische Universität Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Freie Universität Berlin（柏林自由大学）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Google DeepMind（谷歌DeepMind）
Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）；University Health Network（大学网络健康中心）；Peter Munk Cardiac Centre（彼得·芒克心脏中心）；Department of Laboratory Medicine and Pathobiology, University of Toronto（多伦多大学检验医学与病理生物学系）；AI Hub, University Health Network（大学网络健康中心人工智能中心）
CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention
Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Mathematical Institute, University of Oxford（牛津大学数学研究所）；School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航天信息研究所）；Department of Mechanical Engineering, The University of British Columbia（不列颠哥伦比亚大学机械工程系）；College of Life Sciences, Hunan Normal University（湖南师范大学生命科学学院）；School of Electronics, Peking University（北京大学电子学院）
Gather-Scatter Mamba: Accelerating Propagation with Efficient State Space Model
Department of Electrical and Computer Engineering, Sungkyunkwan University（电气与计算机工程系，全北大学）；Department of Artificial Intelligence, Sungkyunkwan University（人工智能系，全北大学）；Hanwha Systems, Republic of Korea（韩华系统，韩国）；Department of Artificial Intelligence, Yonsei University（人工智能系，延世大学）
Phoneme- vs. Character-Level Targets and Selective State-Space Models for Intracortical Brain-to-Text
Universitat Oberta de Catalunya (UOC)（加泰罗尼亚开放大学）；University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））
Block-Biased Mamba for Long-Range Sequence Processing
Center for Applied Mathematics（应用数学中心）；Cornell University（康奈尔大学）；Lawrence Berkeley National Laboratory（伯克利国家实验室）；International Computer Science Institute（国际计算机科学研究所）
