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..
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 | Venue PDF | 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. |