文献类型：期刊文献

英文题名：Research on Payment Attractiveness of Knowledge Contributors in Paid QA Based on Hidden Markov Model

作者：Chen, Wenyu[1]; Cheng, Yan[1]; Feng, Meng[1]

机构：[1] East China University of Science and Technology, Department of Management Science and Engineering, Shanghai, China

年份：2020

卷号：2020-October

起止页码：453

外文期刊名：Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS

收录：EI(收录号：20204709507722)

语种：英文

外文关键词：Decision theory

摘要：Paid QA is in great popularity because it provides askers with the right to choose satisfying knowledge contributors to consult independently, and it has faster responding speed and higher answer quality than traditional nonpaid QA. Based on social capital theory, this paper summarizes influencing factors of payment attractiveness, explores the relationship among payment decision, payment attractiveness and influencing factors such as interactive behaviors in the community by constructing a hidden Markov model. Function of each part of the model is set to reveal the dynamic process of knowledge contributors' payment attractiveness and specific effect of each influencing factor. Data crawled from Zhihu.com are used for model training and parameter estimation. The results show that the state of payment attractiveness can be divided into three (high, medium and low) to best optimize the model. Influencing factors of number of likes, followers, public answers and reviews will play a positive effect on the transition of payment attractiveness state except for a few special cases. Number of honor labels will play a positive effect on the transition of medium and high attractiveness state to payment decision while price plays a negative effect. The results of the study are expected to provide a reference for community operators to efficiently manage knowledge contributors and promote paid QA service to develop better. ? 2020 IEEE.