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 [1].
Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation
Authors: Chen Xu, Yuxin Li, Wenjie Wang, Liang Pang, Jun Xu, Tat-Seng Chua
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted using six large-scale RS backbone models on three publicly available datasets demonstrate that Fair Dual outperforms all baselines in terms of both accuracy and fairness. Our data and codes are shared at https://github.com/Xu Chen0427/Fair Dual. |
| Researcher Affiliation | Academia | 1 Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China 2 School of Information Science and Technology, University of Science and Technology of China, Hefei, China 3 Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 4 NEx T++ Research Center, National University of Singapore, Singapore |
| Pseudocode | Yes | Algorithm 1: Fair Dual |
| Open Source Code | Yes | Extensive experiments conducted using six large-scale RS backbone models on three publicly available datasets demonstrate that Fair Dual outperforms all baselines in terms of both accuracy and fairness. Our data and codes are shared at https://github.com/Xu Chen0427/Fair Dual. |
| Open Datasets | Yes | Datasets. The experiments are conducted on the commonly used two widely used and publicly available recommendation datasets, including MIND (Wu et al., 2020)1, Amazon-Book and Amazon Electronic (He and Mc Auley, 2016)2. Their detailed statistical information is in Appendix I. 1https://microsoftnews.msn.com 2http://jmcauley.ucsd.edu/data/amazon/ |
| Dataset Splits | Yes | Evaluation. We arrange all interactions in the dataset chronologically by their timestamps and employ the first 80% interactions as training data. The remaining 20% of interactions are divided equally, with each 10% segment used for validation and testing, respectively, during evaluation. |
| Hardware Specification | Yes | Environment: our experiments were implemented using Python 3.9 and Py Torch 2.0.1+cu117 (Paszke et al., 2017). All experiments were conducted on a server with an NVIDIA A5000 running Ubuntu 18.04. |
| Software Dependencies | Yes | Environment: our experiments were implemented using Python 3.9 and Py Torch 2.0.1+cu117 (Paszke et al., 2017). All experiments were conducted on a server with an NVIDIA A5000 running Ubuntu 18.04. We implement Fair Dual with the cvxpy (Diamond and Boyd, 2016) for optimization. |
| Experiment Setup | Yes | Hyper-parameter settings: the learning rate η [1e 2, 1e 4] (results shown in Figure 5), and trade-off factor λ [0, 10] (results shown in Figure 3). We set the mg as the group size mg = |Ig|. We also tune sample number Q [50, 400] (results shown in the Table 5), historical length H [3, 7] (results shown in Table 4), freeze parameter updating gap β [128, 3840] (results shown in Figure 4). |