Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1242572.1242638 | WWW |
Keywords | Field | DocType |
supervised rank aggregation,rank aggregation,ranking accuracy,semidefinite programming,markov chain,ranking result,optimization problem,unsupervised method,convex optimization problem,unsupervised learning,supervised learning,convex optimization | Data mining,Learning to rank,Semi-supervised learning,Ranking,Computer science,Markov chain,Supervised learning,Unsupervised learning,Artificial intelligence,Optimization problem,Semidefinite programming,Machine learning | Conference |
Citations | PageRank | References |
53 | 1.73 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-ting Liu | 1 | 188 | 8.95 |
| Tie-yan Liu | 2 | 4662 | 256.32 |
| Tao Qin | 3 | 2384 | 147.25 |
| Zhi-Ming Ma | 4 | 227 | 18.26 |
| Hang Li | 5 | 6294 | 317.05 |