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].
An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations
Authors: Haoran Yang, Xiangyu Zhao, Yicong Li, Hongxu Chen, Guandong Xu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments on three benchmark datasets confirm the effectiveness of CPTPP compared to state-of-the-art baselines. |
| Researcher Affiliation | Academia | Haoran Yang University of Technology Sydney EMAIL Xiangyu Zhao City University of Hong Kong EMAIL Yicong Li University of Technology Sydney EMAIL Hongxu Chen University of Queensland EMAIL Guandong Xu University of Technology Sydney EMAIL |
| Pseudocode | Yes | Algorithm 1: CPTPP algorithm |
| Open Source Code | Yes | The implementation code is available online2 for reproducibility. 2https://github.com/Haoran-Young/CPTPP |
| Open Datasets | Yes | To verify the performance of CPTPP in the recommendation task, we select three popular datasets: Douban [34], Movie Lens-1M [5], and Gowalla [13]. The detailed statistics about the three datasets are listed in Table 1. For each dataset, we randomly select 80% of historical user-item interactions as the training set, and the rest 20% records will serve as the testing set. Appendix A.1 provides the links for downloading these datasets: Douban: https://pan.baidu.com/s/1hr JP6rq#list/path=%2F ML-1M: https://grouplens.org/datasets/movielens/1m/ Gowalla: https://github.com/kuandeng/Light GCN/tree/master/Data/gowalla |
| Dataset Splits | Yes | For each dataset, we randomly select 80% of historical user-item interactions as the training set, and the rest 20% records will serve as the testing set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | To ensure reproducibility, we disclose the comprehensive hyperparameter settings for implementing our proposed CPTPP in the source codes and Appendix A.3. Table 3: Summary of hyper-parameter settings of CPTPP. Hidden dimension size d 64 64 64 Pre-train epoch 10 10 10 Prompt-tune epoch 100 100 100 Batch size 512 512 2048 Learning rate 0.003 0.001 0.001 Regularizer weight λ 0.0001 0.0001 0.0001 Number of GNN layers 2 2 2 Dropout rate 0.1 0.1 0.1 Temperature parameter τ 0.2 0.2 0.2 Prompt size p {8, 16, 32, 64, 128, 256} |