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Fair Max-Min Diversity Maximization in Streaming and Sliding-Window Models
Yanhao Wang1, Francesco Fabbri2, Michael Mathioudakis3
1School of Data Science and Engineering, East China Normal University, Shanghai 200062, China.
Entropy (Basel, Switzerland)
|July 29, 2023
Summary
We developed fast algorithms for fair diversity maximization in data streams. Our methods efficiently select diverse subsets while ensuring group representation, outperforming previous approaches.
Area of Science:
- Computer Science
- Data Science
- Algorithm Design
Background:
- Diversity maximization is crucial for applications like recommender systems.
- Fairness constraints are increasingly important in data analysis.
- Existing fair diversity algorithms are inefficient for streaming data.
Purpose of the Study:
- To develop efficient algorithms for fair max-min diversity maximization.
- To address the challenges of streaming and sliding-window data models.
- To ensure fairness by incorporating group representation constraints.
Main Methods:
- Designed approximation algorithms for the insert-only streaming model.
- Developed approximation algorithms for the sliding-window model.
- Evaluated performance on real-world and synthetic datasets.
Main Results:
- Achieved comparable solution quality to offline algorithms.
- Demonstrated significant speed improvements (orders of magnitude faster).
- Algorithms are efficient for both streaming and sliding-window settings.
Conclusions:
- The proposed algorithms effectively balance diversity and fairness in data streams.
- These methods offer a practical solution for real-time fair diversity maximization.
- The work advances the state-of-the-art in fair data summarization and selection.
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