Abstract
A widespread idea to attack ranking works by reducing it into a set of binary preferences and applying well studied classification techniques. The basic question addressed in this paper relates to whether an accurate classifier would transfer directly into a good ranker. In particular, we explore this reduction for subset ranking, which is based on optimization of DCG metric (Discounted Cumulated Gain), a standard position-sensitive performance measure. We propose a consistent reduction framework, guaranteeing that the minimal DCG regret is achievable by learning pairwise preferences assigned with importance weights. This fact allows us to further develop a novel upper bound on the DCG regret in terms of pairwise regrets. Empirical studies on benchmark datasets validate the proposed reduction approach with improved performance.
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Sun, Z., Jin, W., Wang, J. (2011). Reducing Position-Sensitive Subset Ranking to Classification. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_48
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DOI: https://doi.org/10.1007/978-3-642-21043-3_48
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