Predicting the quality of user-generated answers using co-training in community-based question answering portals

Abstract

Predicting the quality of user-generated answers is definitely of great importance for community-based question answering (CQA) due to the frequent occurrence of low-quality answers. Most existing answer quality prediction works combine non-textual features of user-generated answers directly without considering the diversity of non-textual features. In this paper, we propose two co-training approaches: random subspace split-based co-training (RSS-CoT) and content and social split-based co-training (CS-CoT) to predict the quality of answers by mining the relationships of non-textual features and unlabeled data in CQA. Our results demonstrate that both appropriate combination of non-textual features and unlabeled data can promote the prediction performance of answer quality.