Multimodal DBN for Predicting High-Quality Answers in cQA portals

Haifeng Hu, Bingquan Liu, Baoxun Wang, Ming Liu, Xiaolong Wang

Abstract: In this paper, we address the problem for predicting cQA answer quality as a classification task. We propose a multimodal deep belief nets based approach that operates in two stages: First, the joint representation is learned by taking both textual and non-textual features into a deep learning network. Then, the joint representation learned by the network is used as input features for a linear classifier. Extensive experimental results conducted on two cQA datasets demonstrate the effectiveness of our proposed approach.