Semi-supervised ranking aggregation

Ranking aggregation is a task of combining multiple ranking lists given by several experts or simple rankers to get a hopefully better ranking. It is applicable in several fields such as meta search and collaborative filtering. Most of the existing work is under an unsupervised framework. In these methods, the performances are usually limited especially in unreliable case since labeled information is not involved in. In this paper, we propose a semi-supervised ranking aggregation method, in which preference constraints of several item pairs are given. In our method, the aggregation function is learned based on the ordering agreement of different rankers. The ranking scores assigned by this ranking function on the labeled data should be 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 experimental results on toy data and the OHSUMED data are presented to illustrate the validity of our method.

Research highlights
► A semi-supervised ranking aggregation is proposed.
► The regularized theory and Laplacian based semi-supervised methods is extended.
► The data structure and the labeled preference constraints are involved in.
► These two constraints make the algorithm insensitive to noise.
► The experiment results show our method achieve good results with very few labels.