Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation
Authors: Gaode Chen, Xinghua Zhang, Yijun Su, Yantong Lai, Ji Xiang, Junbo Zhang, Yu Zheng
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three real-world datasets demonstrate that P2FCDR significantly outperforms the state-of-the-art methods and effectively protects data privacy. |
| Researcher Affiliation | Collaboration | 1 Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China 2 School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China 3 JD i City, JD Technology, Beijing, China 4 JD Intelligent Cities Research, Beijing, China |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We study the effectiveness of our P2FCDR on three largest domains on a real-world public dataset Amazon1, i.e., Movies and TV (Movie), Books (Book), and CD Vinyl (Music). 1http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | No | Specifically, we held out the latest interaction as the test set and utilized the remaining data for training. The paper does not explicitly mention a separate validation split or how it was derived. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, processors, or memory used for the experiments. |
| Software Dependencies | No | The paper mentions using Adam as the optimizer but does not provide specific version numbers for any software dependencies or libraries like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | For the representation modeling of users and items, we both use a two-layer fully connected network with dimensions 128 and 128 respectively, and obtain the final embedding dimension k as 128. Considering the trade-off between recommendation performance and privacy protection, we set λ to 0.02. For the learning of gated selecting vector, we use a two-layer fully connected network with dimension 128 and 128, respectively. When training our models, we choose Adam as the optimizer, and set the learning rate to 0.001. Meanwhile, we select a batch of users according to the IDs of the common user to construct minibatches, and set the batch size to 256. |