Semi-supervised ranking aggregation

Authors:
Shouchun Chen;Fei Wang;Yaangqiu Song;Changshui Zhang
Affiliations:
Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
Proceedings of the 17th ACM conference on Information and knowledge management
Year:
2008

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Abstract

Ranking aggregation is important in data mining and information retrieval. In this paper, we proposed a semi-supervised ranking aggregation method, in which the order of several item pairs are labeled as side information. The core idea is to learn a ranking function based on the ordering agreement of different rankers. The ranking scores assigned by this ranking function on the labeled data are consistent with the given pairwise order constraints while the ranking scores on the unlabeled data obey the intrinsic manifold structure of the rank items. The experiment results show our method work well.