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A Self-Training Listwise Method for Learning to Rank with Partially Labeled Data
| Content Provider | CiteSeerX |
|---|---|
| Author | He, Hai-Jiang |
| 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. Keywords-learning to rank; semi-supervised; listwise; self-training; document retrieval 1. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
