Semi-supervised Ranking via List-Wise Approach

Abstract

Semi-supervised ranking is a relatively new and important learning problem. Some semi-supervised learning to rank algorithms have been proposed by adapting existing pair-wise ranking algorithms into the semi-supervised setting. In contrast, there is comparatively little work in developing semi-supervised ranking algorithm based on the list-wise approach. In this paper, we proposed a semi-supervised extension of one of the state-of-the art list-wise ranking algorithm—AdaRank, for the task of learning to rank with partially labeled data. The proposed algorithm is a two-staged approach that combines the process of label propagation and a regularized version of AdaRank. The label propagation process is used to propagate the relevance labels from labeled instances to other unlabeled ones, so that more training data are available to learn the ranking function. The regularized version of AdaRank optimizes a novel performance measure, which incorporates both the usual Information Retrieval (IR) metrics and the smoothness of the ranking function. Experimental results on benchmark datasets show the advantage of our methods over existing pair-wise algorithms for the Information Retrieval measures.