Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach
Authors: Jinqiu Jin, Haoxuan Li, Fuli Feng, Sihao Ding, Peng Wu, Xiangnan He
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two realworld datasets validate the effectiveness of our approach. |
| Researcher Affiliation | Collaboration | 1University of Science and Technology of China 2Peking University 3Beijing Technology and Business University |
| Pseudocode | Yes | Algorithm 1: Social-Aware Flexible Fair Recommendation with Controllable Accuracy (So FA) |
| Open Source Code | Yes | Our codes are available at https://github.com/mitao-cat/nips23_social_igf. |
| Open Datasets | Yes | Kuai Rec is a short-video recommendation dataset on the video-sharing platform Kuaishou3... Epinions is a dataset derived from a trust network... |
| Dataset Splits | Yes | Throughout our experiment, we randomly split the interactions of each dataset into training set (60%), validation set (20%), and testing set (20%). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper mentions using 'BPRMF [9] as the backbone' but does not specify version numbers for programming languages, libraries, or other software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | For all compared methods, we use a pre-trained BPRMF [9] as the backbone with fixed optimal batch size and regularization coefficient, then fine-tune the model by using the baselines methods for achieving IGF notions... The learning rate for all methods is searched within {0.03lr, 0.1lr, 0.3lr, lr, 3lr, 10lr}, and the coefficients of IGF terms for regularization-based and post-processing baselines are searched within {0.1, 0.2, 0.5, 1, 2, 5}. The early stopping strategy is employed when the F1SP or F1EO value does not decrease over 5 epochs. |