Reproducibility Companion Paper: Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
This is an incremental reproducibility study for researchers in recommender systems and machine learning.
The paper reproduces experimental results from a prior work on attribute-wise unlearning in recommender systems to validate the method's effectiveness and aid reproducibility.
In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which was published in the proceedings of the 31st ACM International Conference on Multimedia. This paper aims to validate the effectiveness of our proposed method and help others reproduce our experimental results. We provide detailed descriptions of our preprocessed datasets, source code structure, configuration file settings, experimental environment, and reproduced experimental results.