Statistical Analysis of Bayes Optimal Subset Ranking

13 years 5 months ago
Statistical Analysis of Bayes Optimal Subset Ranking
Abstract--The ranking problem has become increasingly important in modern applications of statistical methods in automated decision making systems. In particular, we consider a formulation of the statistical ranking problem which we call subset ranking, and focus on the discounted cumulated gain (DCG) criterion that measures the quality of items near the top of the rank-list. Similar to error minimization for binary classification, direct optimization of natural ranking criteria such as DCG leads to a nonconvex optimization problems that can be NP-hard. Therefore, a computationally more tractable approach is needed. We present bounds that relate the approximate optimization of DCG to the approximate minimization of certain regression errors. These bounds justify the use of convex learning formulations for solving the subset ranking problem. The resulting estimation methods are not conventional, in that we focus on the estimation quality in the top-portion of the rank-list. We further i...
David Cossock, Tong Zhang
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TIT
Authors David Cossock, Tong Zhang
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