Cost-Effective On-Device Sequential Recommendation with Spiking Neural Networks

Yu, Di; Lv, Changze; Du, Xin; Jiang, Linshan; Yin, Qing; Tong, Wentao; Zheng, Xiaoqing; Deng, Shuiguang

doi:10.24963/ijcai.2025/398

Cost-Effective On-Device Sequential Recommendation with Spiking Neural Networks

Di Yu, Changze Lv, Xin Du, Linshan Jiang, Qing Yin, Wentao Tong, Xiaoqing Zheng, Shuiguang Deng

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 3579-3587.

https://doi.org/10.24963/ijcai.2025/398

Keywords

Humans and AI: HAI: Applications
Machine Learning: ML: Applications
Data Mining: DM: Recommender systems
Humans and AI: HAI: Brain sciences

Abstract

On-device sequential recommendation (SR) systems are designed to make local inferences using real-time features, thereby alleviating the communication burden on server-based recommenders when handling concurrent requests from millions of users.

However, the resource constraints of edge devices, including limited memory and computational capacity, pose significant challenges to deploying efficient SR models.

Inspired by the energy-efficient and sparse computing properties of deep Spiking Neural Networks (SNNs), we propose a cost-effective on-device SR model named SSR, which encodes dense embedding representations into sparse spike-wise representations and integrates novel spiking filter modules to extract temporal patterns and critical features from item sequences, optimizing computational and memory efficiency without sacrificing recommendation accuracy.

Extensive experiments on real-world datasets demonstrate the superiority of SSR. Compared to other SR baselines, SSR achieves comparable recommendation performance while reducing energy consumption by an average of 59.43%. In addition, SSR significantly lowers memory usage, making it particularly well-suited for deployment on resource-constrained edge devices.

Di Yu, Changze Lv, Xin Du, Linshan Jiang, Qing Yin, Wentao Tong, Xiaoqing Zheng, and Shuiguang Deng. 2025. Cost-Effective On-Device Sequential Recommendation with Spiking Neural Networks. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 3579–3587. https://doi.org/10.24963/ijcai.2025/398

@inproceedings{ijcai2025p398,
  title     = {Cost-Effective On-Device Sequential Recommendation with Spiking Neural Networks},
  author    = {Yu, Di and Lv, Changze and Du, Xin and Jiang, Linshan and Yin, Qing and Tong, Wentao and Zheng, Xiaoqing and Deng, Shuiguang},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {3579--3587},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/398},
  url       = {https://doi.org/10.24963/ijcai.2025/398},
}

TY  - CPAPER
TI  - Cost-Effective On-Device Sequential Recommendation with Spiking Neural Networks
AU  - Yu, Di
AU  - Lv, Changze
AU  - Du, Xin
AU  - Jiang, Linshan
AU  - Yin, Qing
AU  - Tong, Wentao
AU  - Zheng, Xiaoqing
AU  - Deng, Shuiguang
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 3579
EP  - 3587
DO  - 10.24963/ijcai.2025/398
UR  - https://doi.org/10.24963/ijcai.2025/398
ER  -