Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-end Learning for Fair Ranking Systems   The learning-to-rank problem aims at ranking items to maximize exposure of
those most relevant to a user query. A desirable property of such ranking
systems is to guarantee some notion of fairness among specified item groups.
While fairness has recently been considered in the context of learning-to-rank
systems, current methods cannot provide guarantees on the fairness of the
proposed ranking policies.
  This paper addresses this gap and introduces Smart Predict and Optimize for
Fair Ranking (SPOFR), an integrated optimization and learning framework for
fairness-constrained learning to rank. The end-to-end SPOFR framework includes
a constrained optimization sub-model and produces ranking policies that are
guaranteed to satisfy fairness constraints while allowing for fine control of
the fairness-utility tradeoff. SPOFR is shown to significantly improve current
state-of-the-art fair learning-to-rank systems with respect to established
performance metrics.

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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
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The categories of this paper are: econ
12
The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th