Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
GPL4SRec: Graph Multi-Level Aware Prompt Learning for Streaming Recommendation
Authors: Hao Cang, Huanhuan Yuan, Jiaqing Fan, Lei Zhao, Guanfeng Liu, Pengpeng Zhao
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results also prove that our model significantly outperforms the state-of-the-art approaches in SRec. ... 5 Experiments In this section, we conduct experiments with the aim of answering the following questions: Q1: How do our proposed GPL4SRec perform compared with other baselines? Q2: What is the influence of key components of GPL4SRec? Q3: How is the robustness of GPL4SRec? Q4: Whether is GPL4SRec sensitive to the hyper-parameters? Q5: How efficient is the training of GPL4SRec in streaming scenarios? |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Soochow University, China 2Department of Computing, Macquarie University, Australia EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using textual descriptions and mathematical formulas, for example, in Section 4 'Methodology' and its subsections '4.1 Graph Pre-training' and '4.2 Graph Prompt Learning'. It does not contain a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | Datasets. We use three public datasets that cover diverse real-world scenarios in streaming recommendation. The Taobao dataset records the implicit feedback from taobao.com, which is a Chinese e-commerce platform, during a period of 10 days. The Koubei dataset, which is provided for the IJCAI 16 contest, documents 9 weeks worth of user interactions with local stores on Koubei within Alipay. The Amazon dataset is composed of a collection of product reviews from Amazon that spans 13 weeks. |
| Dataset Splits | Yes | Evaluation Protocols. In our evaluation, we simulate real-world dynamics using graph snapshots taken at different intervals (weekly/daily). We use a two-step sliding window to learn from current data and predict future changes. Following the Pre-train and Fine-tune paradigm, we pre-train on most of the dataset, fine-tune, and evaluate on later snapshots (see Table 2). For consistency, the same method is applied to all baselines. ... Table 2: Statistics and temporal segmentation of experiment dataset. ... Temporal Segmentation # Pre-training Span 4 weeks 4 weeks 5 days # Tuning-Predicting Span 9 weeks 5 weeks 5 days # Snapshot Granularity weekly weekly daily |
| Hardware Specification | Yes | Implementation Details. We run all methods in Py Torch [Paszke et al., 2017] with Adam [Diederik, 2014] optimizer on an NVIDIA Ge Force 4070Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Adam optimizer' but does not specify their version numbers or other key software dependencies with versions. |
| Experiment Setup | Yes | Implementation Details. We run all methods in Py Torch [Paszke et al., 2017] with Adam [Diederik, 2014] optimizer on an NVIDIA Ge Force 4070Ti GPU. In our experiment, the batch size b and the demension of embeddings d are set 2048 and 64. The layers k of GNNs are set 3. We train all models 300 epochs at every snapshot. We apply grid search to find the optimal hyper-parameters for each model. The ranges of hyper-parameters are [16, 32, 64, 128] for the size N of nodeaware prompts NP, the size S of structure-aware prompts SP and the size of layer-aware prompts LP. GPL4SRec is trained with a learning rate of 0.001. |