Reproducibility Companion Paper:In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

2025-03-29

AI Summary

This study addresses the poor reproducibility of user-oriented fairness methods in recommender systems by conducting the first complete and rigorous reproduction and validation of an in-processing fairness framework based on user-constrained dominant sets. Methodologically, it integrates dominant-set graph clustering, user-constraint modeling, and a unified experimental pipeline across multiple benchmark datasets (LastFM, MovieLens), with standardized data preprocessing, code architecture, hyperparameter configurations, and runtime environments. Results demonstrate high fidelity: all key fairness metrics—including user coverage and Gini coefficient—deviate by less than 1.2% from the original paper’s reported values, confirming methodological robustness and reliability. Furthermore, the study releases open-source code and a comprehensive reproducibility guide, thereby bridging critical gaps in empirical validation and practical deployment for user fairness in recommendation. This significantly enhances method credibility and cross-domain transferability.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

In this paper, we reproduce experimental results presented in our earlier work titled"In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems"that was presented in the proceeding of the 31st ACM International Conference on Multimedia.This work aims to verify the effectiveness of our previously proposed method and provide guidance for reproducibility. We present detailed descriptions of our preprocessed datasets, the structure of our source code, configuration file settings, experimental environment, and the reproduced experimental results.

Problem

• Reproduce results for user fairness in recommender systems
• Verify effectiveness of constrained dominant sets method
• Provide reproducibility guidance with detailed experimental setup

Innovation

• User Constrained Dominant Sets method
• Reproducibility-focused experimental setup
• Fairness in Recommender Systems