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Approval-based shortlisting.

Martin Lackner1, Jan Maly1

  • 1DBAI, TU Wien, Vienna, Austria.

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Summary
This summary is machine-generated.

This study analyzes shortlisting methods using approval data, where the shortlist size is variable. It provides recommendations for choosing the best shortlisting approach based on desired properties.

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Area of Science:

  • Decision Science
  • Computer Science
  • Recommender Systems

Background:

  • Shortlisting reduces extensive options to a manageable set, crucial for award nominations and recommender systems.
  • Approval data, a common preference type, is frequently used in shortlisting processes.
  • Existing shortlisting methods often assume a fixed shortlist size, which may not always be optimal.

Purpose of the Study:

  • To analyze shortlisting methods based on approval data.
  • To investigate shortlisting methods where the shortlist size is not fixed but determined by the method itself.
  • To provide guidance on selecting appropriate shortlisting methods based on specific requirements.

Main Methods:

  • Axiomatic analysis of established and novel shortlisting methods.
  • Experimental evaluation using both synthetic and real-world datasets.
  • Comparison of different shortlisting algorithms based on their properties.

Main Results:

  • Identification of key properties and trade-offs among various shortlisting methods.
  • Demonstration of how method choice impacts shortlist quality and characteristics.
  • Empirical evidence supporting the effectiveness of certain methods under different conditions.

Conclusions:

  • The choice of shortlisting method significantly influences the outcome and should align with desired properties.
  • Variable shortlist size methods offer flexibility and can be advantageous in certain applications.
  • The study offers practical recommendations for optimizing shortlisting processes in diverse contexts.