Cost-Effective On-Device Sequential Recommendation with Spiking Neural Networks
Authors: Di Yu, Changze Lv, Xin Du, Linshan Jiang, Qing Yin, Wentao Tong, Xiaoqing Zheng, Shuiguang Deng
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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%. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Fudan University 3National University of Singapore 4JD.com EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Model Inference of SSR. Input: Input sequences x, top value k Output: Top-k recommendation list Pk 1: Query spike-wise representation X with x. 2: Encoding X to H. 3: for all filter block l {1, ..., L} do 4: Learnable 1D-DFT: Hl = F(Hl) Wl. 5: 1D-IDFT: Fl = F 1( Hl). 6: Convert Fl to spikes with SN( ). 7: end for 8: Densify: HL Linear( FL). 9: Computing preference scores P with HL and X . 10: Sort P in a descending order. 11: Cut out top-k items from P to form Pk. 12: return Pk |
| Open Source Code | Yes | 1https://github.com/AmazingDD/serenRec |
| Open Datasets | Yes | Five public datasets collected from various platforms are selected to evaluate the efficacy of SSR. Their corresponding statistics are summarized in Table 1, sorted by density. Following [Zhou et al., 2022], we group the records by users or sessions, sort them by time in ascending order, and adopt a 5-core strategy for all datasets to ensure each user and item has at least five interaction records. |
| Dataset Splits | Yes | We use the time-aware and user-level split-by-ratio strategy [Sun et al., 2022] to split the whole dataset, where the last 20% of the total item sequences is the test set. |
| Hardware Specification | Yes | We assume running SSR on a 45nm neuromorphic hardware [Horowitz, 2014] and other baselines on GPUs, since SNNs can demonstrate low computing energy costs when deployed on neuromorphic chips, and GPUs are the most suitable platform for executing ANNs. |
| Software Dependencies | No | The paper mentions "Torch" as an implementation tool but does not provide a specific version number. No other specific software dependencies with versions are listed. |
| Experiment Setup | Yes | By default, we implement all models with Torch and use Adam optimizer with the 10 3 learning rate. The embedding dimension for each item is 64, and the time step size T for LIF neurons is 4. |