A self-training listwise method for learning to rank with partially labeled data

Hai-jiang He

doi:10.1117/12.913453

1 October 2011 A self-training listwise method for learning to rank with partially labeled data

Hai-jiang He

Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 828560 (2011) https://doi.org/10.1117/12.913453
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt

Abstract

Recently there has been a general direction in ranking algorithms that combine labeled and unlabeled data in document retrieval. Previous work for learning to rank has focused on cases where only the pairwise approach is available for essential ranking algorithms. This paper addresses the semi-supervised ranking problems where the listwise approach is used to construct ranking models. The method is an iterative self-training algorithm that in each iteration a ranking function is built by learning from the current set of labeled queries. The newly learned ranking function is produced, then it is used to teaching unlabeled query. The likelihood loss is employed to evaluate the similarity of two permutations for a given query. The experimental results show the effectiveness of the method proposed in this paper.

Citation Download Citation

Hai-jiang He "A self-training listwise method for learning to rank with partially labeled data", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828560 (1 October 2011); https://doi.org/10.1117/12.913453

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