Preference relationship-based CrossCMN scheme for answer ranking in community QA

Community question answering (CQA) systems aim to provide users with high-quality answers. Nevertheless, unreliable answers are often returned to users in CQA systems, and the phenomenon causes that users have to browse multiple answers to find the best one. To improve such problem, we design a novel scheme, named PW-CrossCMN. The scheme ranks the candidate answers by pair-wise approach based on numerous historical documents. In the scheme, we apply the preference relationship into deep learning framework. Specifically, the scheme consists of two phases. In phase 1, the scheme extracts the features via automated feature engineering to construct the preference vectors and then divides the vectors into balanced positive and negative training samples based on the preference relationship. In phase 2, we build the CrossCMN model, which implements the multi-network parallel convolution and the cross forward propagation of full-connected layers, to achieve training and prediction tasks. Moreover, the multi-layer perception (MLP) is introduced to extract combination features in the prediction module. We perform extensive experiments on two typical datasets, and the results show that our scheme has more excellent performance in answer ranking task compared with several state-of-the-art baselines. In addition, we have released the relevant codes.