Ranking Tweets by Labeled and Collaboratively Selected Pairs with Transitive Closure

Tweets ranking is important for information acquisition in Microblog. Due to the content sparsity and lack of labeled data, it is better to employ semi-supervised learning methods to utilize the unlabeled data. However, most of previous semi-supervised learning methods do not consider the pair conflict problem, which means that the new selected unlabeled data may have order conflict with the labeled and previously selected data. It will hurt the learning performance, if the training data contains many conflict pairs. In this paper, we propose a new collaborative semi-supervised SVM ranking model (CSR-TC), selecting unlabeled data based on a dynamically maintained transitive closure graph to avoid pair conflict. We also investigate the two views of features, intrinsic and content-relevant features, for the proposed model. Extensive experiments are conducted on TREC Microblogging corpus. The results demonstrate that our proposed method achieves significant improvement, compared to several state-of-the-art models.