Metadata-Based Clustered Multi-task Learning for Thread Mining in Web Communities.

With user-generated content explosively growing, how to find valuable posts from discussion threads in web communities becomes a hot topic. Although many learning algorithms have been proposed for mining the thread contents, there are still two problems that are not effectively considered. First, the learning algorithms are usually complicated so as to deal with various kinds of threads in web communities, which damages the generalization performance of the algorithms and takes the risk of overfitting to the learning models. Second, the small sample size problem exists when the training data for learning is divided into many isolated groups and each group is trained separately in order to avoid overfitting. In this paper, we propose a metadata-based clustered multi-task learning method, which takes full use of the metadata of threads and fuses it in the multi-task learning based on a divide-and-learn strategy. Our method provides an effective solution to the above problems by finding the geometric structure or context of semantics of threads in web communities and constructing the relations among training thread groups and their corresponding learning tasks. In addition, a soft-assigned clustered multi-task learning model is employed. Our experimental results show the effectiveness of our method.