document: Supervised rank aggregation This paper is concerned with rank aggregation , the task of combining the ranking results of individual rankers at meta-search . Previously , rank aggregation was performed mainly by means of unsupervised learning . To further enhance ranking accuracies , we propose employing supervised learning to perform the task , using labeled data . We refer to the approach as Supervised Rank Aggregation . We set up a general framework for conducting Supervised Rank Aggregation , in which learning is formalized an optimization which minimizes disagreements between ranking results and the labeled data . As case study , we focus on Markov Chain based rank aggregation in this paper . The optimization for Markov Chain based methods is not a convex optimization problem , however , and thus is hard to solve . We prove that we can transform the optimization problem into that of Semidefinite Programming and solve it efficiently . Experimental results on meta-searches show that Supervised Rank Aggregation can significantly outperform existing unsupervised methods .
doc_bio_tags: O B I O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O