@inproceedings{zhang2025glint,
  author = {Zhang, Sheng and Wang, Maolin and Wang, Wanyu and Gao, Jingtong and Zhao, Xiangyu and Yang, Yu and Wei, Xuetao and Liu, Zitao and Xu, Tong},
  title = {GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems},
  year = {2025},
  isbn = {9798400712456},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3690624.3709304},
  doi = {10.1145/3690624.3709304},
  abstract = {Transformer-based models have gained significant traction in sequential recommender systems (SRSs) for their ability to capture user-item interactions effectively. However, these models often suffer from high computational costs and slow inference. Meanwhile, existing efficient SRS approaches struggle to embed high-quality semantic and positional information into latent representations. To tackle these challenges, this paper introduces GLINT-RU, a lightweight and efficient SRS leveraging a single-layer dense selective Gated Recurrent Units (GRU) module to accelerate inference. By incorporating a dense selective gate, GLINT-RU adaptively captures temporal dependencies and fine-grained positional information, generating high-quality latent representations. Additionally, a parallel mixing block infuses fine-grained positional features into user-item interactions, enhancing both recommendation quality and efficiency. Extensive experiments on three datasets demonstrate that GLINT-RU achieves superior prediction accuracy and inference speed, outperforming baselines based on RNNs, Transformers, MLPs, and SSMs. These results establish GLINT-RU as a powerful and efficient solution for SRSs. The implementation code is publicly available for reproducibility. https://github.com/szhang-cityu/GLINT-RU.},
  booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1},
  pages = {1948–1959},
  numpages = {12},
  keywords = {efficient model, gated recurrent units, recommender systems, sequential recommender systems},
  location = {Toronto ON, Canada},
  series = {KDD '25}
}