A Deep Learning Approach for Expert Identification in Question Answering Communities
This addresses the problem of efficiently finding experts in online communities, but it is incremental as it builds on existing neural network methods with a new feature combination approach.
The paper tackles expert identification in question answering communities by proposing a convolutional neural network that combines user and question feature representations, achieving top-1 accuracy improvements over baselines on Stack Overflow and Zhihu datasets.
In this paper, we describe an effective convolutional neural network framework for identifying the expert in question answering community. This approach uses the convolutional neural network and combines user feature representations with question feature representations to compute scores that the user who gets the highest score is the expert on this question. Unlike prior work, this method does not measure expert based on measure answer content quality to identify the expert but only require question sentence and user embedding feature to identify the expert. Remarkably, Our model can be applied to different languages and different domains. The proposed framework is trained on two datasets, The first dataset is Stack Overflow and the second one is Zhihu. The Top-1 accuracy results of our experiments show that our framework outperforms the best baseline framework for expert identification.