A New Method For Personnel Selection Based On Ranking Aggregation Using A Reinforcement Learning Approach

Yaima Filiberto Cabrera, Rafael Bello Pérez, Ann Nowe


In this paper propose a new approach to the problem of aggregating rankings for obtaining an overall ranking. This is also referred to as the aggregation ranking in the personnel selection problem. Our approach is based on a distance measure between the individual and the overall ranking, and looks for the solution that minimizes the disagreement between the input rankings and the resulting aggregation. The method uses a reinforcement learning approach to build the aggregation and its performance and comparison with other approaches shows promising results.


aggregating rankings, personnel selection, reinforcement learning

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