SIGMA: Selective Gated Mamba for Sequential Recommendation
Authors: Ziwei Liu, Qidong Liu, Yejing Wang, Wanyu Wang, Pengyue Jia, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that SIGMA significantly outperforms existing baselines across five real-world datasets. |
| Researcher Affiliation | Academia | 1City University of Hong Kong 2School of Auto. Science & Engineering, MOEKLINNS Lab, Xi an Jiaotong University 3Jinan University 4Jilin University |
| Pseudocode | No | The paper describes the methodology using mathematical formulas and descriptive text in sections like 'Methodology' and 'G-Mamba Block', but no explicitly labeled pseudocode or algorithm blocks are present. |
| Open Source Code | Yes | Code https://github.com/Applied-Machine-Learning Lab/SIMGA |
| Open Datasets | Yes | We conduct comprehensive experiments on five representative real-world datasets i.e., Yelp1, Amazon series2 (Beauty, Sports and Games) and Movie Lens-1M3.The statistics of datasets after preprocessing are shown in Table 1. ... 1https://www.yelp.com/dataset 2https://cseweb.ucsd.edu/jmcauley/datasets.html#amazon reviews 3https://grouplens.org/datasets/movielens/ |
| Dataset Splits | No | The paper describes categorizing users into subsets based on interaction length for analysis ('Short (0 5), Medium (5 20), and Long (20+)'), but it does not explicitly provide details on how the datasets are split into training, validation, and test sets for model evaluation, such as specific percentages or sample counts. |
| Hardware Specification | Yes | For GPU selection, all experiments are conducted on a single NVIDIA L4 GPU. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and setting a learning rate, but it does not specify versions for any programming languages, libraries, or other software components used in the implementation. |
| Experiment Setup | Yes | The Adam optimizer (Kingma and Ba 2014) is used with a learning rate of 0.001. For a fair comparison, the embedding dimension for all tested models is set to 64. |