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 | Conference PDF | Archive PDF | Plain Text | 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. |