Abstract
Community Question Answering (CQA) websites are becoming increasingly important sources of information where users can share knowledge on various topics. These websites provide many opportunities for users to seek for help and provide answers, but they also bring new challenges. One of the challenges is that most new questions posted everyday cannot be routed to appropriate users who can answer them. It means that experts are not provided with questions matching their expertise, and therefore new questions cannot be answered in time. Our main goal is to find which user has more potential to be the best answerer for a newly posted question. In this paper, we propose an approach which based on convolutional neural networks (CNN) to predict the best answerer for a new posted question on CQA websites. We have applied our model on the dataset downloaded from StackOverflow, one of the biggest CQA sites. The results show that our approach performs better than Segmented Topic Model.
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Acknowledgments
We thank the National Key Technology R&D Program (2015BAF20B02), the Natural Science Foundation of China (61272373, 61572098, 61300088), the Liaoning Province Natural Science Foundation of China (2014020003) for the funding support.
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Wang, J., Sun, J., Lin, H., Dong, H., Zhang, S. (2016). Predicting Best Answerers for New Questions: An Approach Leveraging Convolution Neural Networks in Community Question Answering. In: Li, Y., Xiang, G., Lin, H., Wang, M. (eds) Social Media Processing. SMP 2016. Communications in Computer and Information Science, vol 669. Springer, Singapore. https://doi.org/10.1007/978-981-10-2993-6_3
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DOI: https://doi.org/10.1007/978-981-10-2993-6_3
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