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..
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 | Venue PDF | 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. |