Fair Representation Learning for Recommendation: A Mutual Information Perspective
Authors: Chen Zhao, Le Wu, Pengyang Shao, Kun Zhang, Richang Hong, Meng Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments over two real-world datasets demonstrate the effectiveness of our proposed Fair MI in reducing unfairness and improving recommendation accuracy simultaneously. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Information Engineering, Hefei University of Technology 2 Hefei Comprehensive National Science Center |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (e.g., a clearly labeled 'Algorithm' section or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We conduct experiments on two datasets: Movie Lens-1M (Harper and Konstan 2015) and Lastfm-360K (Celma Herrada et al. 2009). |
| Dataset Splits | Yes | On Movie Lens-1M, We split the historical records into training set and test set with the ratio of 8:2, and 10% of the test set is used as validation. |
| Hardware Specification | Yes | The experiments are implemented with Pytorch-1.7.0 on 1 NVIDIA TITAN-RTX GPU. |
| Software Dependencies | Yes | The experiments are implemented with Pytorch-1.7.0 on 1 NVIDIA TITAN-RTX GPU. |
| Experiment Setup | Yes | We set the embedding size as D = 64, the mini-batch size is set to 2048 for Movielens-1M and 4096 for Lastfm-360K; and choose the Adam optimizer with the initial learning rate equaling 0.001. |