Abstract
The quality of the ranking answer is good or bad, directly affects the high quality answers for users in the community question answering system. Learning method by sorting, establish the answer ranking model, is a research hotspot in community question answering system. The characteristics of tags and behavior of users, often have a direct relationship with the answer to the users’ expectations. In this paper, ListNet is used as the ranking method which selects Neural Networks as the model and Gradient Descent as the optimization method to structure ListNet ranking model which blends in characteristics of tags and behaviors of user. Then, the ranking mode is utilized to finish experiment combining the answers feature space, and the result of experiment shows that the ListNet ranking model can improve effect of answers ranking obviously which blends in the characteristics of tags and behaviors of users.
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Acknowledgement
This work is supported by the National Science Foundation on of China under the Grant No. 61365010.
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Wang, Q., Su, L., Li, Y., Liu, J. (2018). Answer Ranking by Analyzing Characteristic of Tags and Behaviors of Users. In: Wan, J., et al. Cloud Computing, Security, Privacy in New Computing Environments. CloudComp SPNCE 2016 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 197. Springer, Cham. https://doi.org/10.1007/978-3-319-69605-8_6
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