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..
Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning
Authors: Zhenghong Lin, Wei Huang, Hengyu Zhang, Jiayu Xu, Weiming Liu, Xinting Liao, Fan Wang, Shiping Wang, Yanchao Tan
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets validate that our proposed P2DTR framework achieves superior utility under a privacy-preserving guarantee on both domains. Finally, we evaluate the proposed P2DTR with extensive experiments on four real-world benchmark datasets for federated DTCDR. Extensive experimental results show that the proposed P2DTR is able to significantly improve the recommendation performance under privacy-preserving scenarios over all baselines. |
| Researcher Affiliation | Academia | 1College of Computer and Data Science, Fuzhou University, Fuzhou, China 2College of Computer Science and Technology, Zhejiang University, Hangzhou, China |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | On the basis of the previous works, we build our scenarios using the chosen cross-domain recommendation datasets [Zhu et al., 2022], and the preprocessing settings with two domains (K = 2). In particular, we carry out experiments on the large-scale public Amazon datasets. Note that, we also conduct experiments under the multi-domain scenarios on Douban datasets with (K = 3), which follows [Liu et al., 2023c]. |
| Dataset Splits | Yes | For each user, we use the first 40% of data as the training set, 30% data as the validation set, and 30% data as the testing set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | For the common hyperparameters in the baselines, we adopt the same value for all the methods, such as the embedding dimension d to 128, the batch size to 1024 and the minibatch size to 128 or 256. For our proposed model P2DTR, we tune the hyperparameter λ in {102, 10, 1, 1e 1, 1e 2}, the number of prototypes in {16, 32, 64, 128} and the number of graph encoder layer in {1, 2, 3, 4}. In our model, we use Adam optimizer and the decay learning rate. |