Abstract
Recently, more and more people choose to seek health-related information in health-related on-line QA communities. Doctor recommendation is very essential for users in these communities since it is difficult for them to find a proper doctor without assistance from medical staffs. In this paper, we develop a Generative Adversarial Nets (GANs)-based doctor recommendation framework utilizing data in Chinese on-line QA communities. We conduct extensive sets of experiments on a real-world dataset. The experimental results show that our framework significantly outperforms the state-of-the-art baselines.
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Acknowledgement
This work was supported by NSFC(91646202), National Key R&D Program of China(SQ2018YFB140235), and the 1000-Talent program.
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Tian, B., Zhang, Y., Chen, X., Xing, C., Li, C. (2019). DRGAN: A GAN-Based Framework for Doctor Recommendation in Chinese On-Line QA Communities. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_63
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DOI: https://doi.org/10.1007/978-3-030-18590-9_63
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