Issue No. 08 - Aug. (2017 vol. 29)
Yuan Yao , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing Shi, Jiangsu Sheng, China
Hanghang Tong , Arizona State University, Tempe, AZ 85281
Feng Xu , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing Shi, Jiangsu Sheng, China
Jian Lu , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing Shi, Jiangsu Sheng, China
Community Question Answering (CQA) sites, such as Stack Overflow and Yahoo! Answers, have become very popular in recent years. These sites contain rich crowdsourcing knowledge contributed by the site users in the form of questions and answers, and these questions and answers can satisfy the information needs of more users. In this article, we aim at predicting the voting scores of questions/answers shortly after they are posted in the CQA sites. To accomplish this task, we identify three key aspects that matter with the voting of a post, i.e., the non-linear relationships between features and output, the question and answer coupling, and the dynamic fashion of data arrivals. A family of algorithms are proposed to model the above three key aspects. Some approximations and extensions are also proposed to scale up the computation. We analyze the proposed algorithms in terms of optimality, correctness, and complexity. Extensive experimental evaluations conducted on two real data sets demonstrate the effectiveness and efficiency of our algorithms.
Heuristic algorithms, Prediction algorithms, Couplings, Kernel, Lips, Linearity, Predictive models
Y. Yao, H. Tong, F. Xu and J. Lu, "Scalable Algorithms for CQA Post Voting Prediction," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 8, pp. 1723-1736, 2017.