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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)
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Summary
This summary is machine-generated.

We developed fast algorithms for fair diversity maximization in data streams. Our methods efficiently select diverse subsets while ensuring group representation, outperforming previous approaches.

Keywords:
diversity maximizationgroup fairnesssliding-window algorithmstreaming algorithm

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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.