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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Graph Disentangled Contrastive Learning with Personalized Transfer for Cross-Domain Recommendation
Authors: Jing Liu, Lele Sun, Weizhi Nie, Peiguang Jing, Yuting Su
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on four real-world datasets demonstrate the superiority of GDCCDR over state-of-the-art methods. |
| Researcher Affiliation | Academia | Jing Liu, Lele Sun, Weizhi Nie, Peiguang Jing, Yuting Su* School of Electrical and Information Engineering, Tianjin University, China EMAIL |
| Pseudocode | No | The paper describes its methods using mathematical formulations and textual descriptions but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We evaluate GDCCDR on the Amazon dataset1, specifically Sport&Phone, Sport&Cloth, Elec&Phone, and Elec&Cloth. 1http://jmcauley.ucsd.edu/data/amazon/index 2014.html |
| Dataset Splits | No | The paper mentions training and test sets and a leave-one-out strategy but does not explicitly provide percentages or counts for distinct training, validation, and test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch implementation' but does not provide specific version numbers for software dependencies like PyTorch or Python. |
| Experiment Setup | Yes | The embedding dimension (d) is set to 128 for all methods, with a fixed learning rate of 0.001, a batch size of 1024, and a dropout rate of 0.5. The low-rank (k) is 10, the proximate temperature (τp) is 0.05, the L2 regularization coefficient (λl) is selected from {0.05, 0.005, 0.0005}. The final embeddings of GNN-based methods are obtained through mean pooling. For point-wise loss, we have four negative samples per positive sample. |