A Co-Ranking Algorithm for Learning Listwise Ranking Functions from Unlabeled Data

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Provided by: Academy Publisher
Topic: Data Management
Format: PDF
In this paper, the authors propose a co-ranking algorithm that trains list-wise ranking functions using unlabeled data simultaneously with a small number of labeled data. The co-ranking algorithm is based on the co-training paradigm that is a very common scheme in the semi-supervised classification framework. First, they use two list-wise ranking methods to construct base ranker and assistant ranker, respectively, by learning from the current labeled set. Then they score documents of unlabeled query set by these rankers.
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