2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2014)
Aug. 17, 2014 to Aug. 20, 2014
Yuan Yao , State Key Laboratory for Novel Software Technology, Nanjing University, China
Hanghang Tong , City College, CUNY, USA
Tao Xie , University of Illinois at Urbana-Champaign, USA
Leman Akoglu , Stony Brook University, USA
Feng Xu , State Key Laboratory for Novel Software Technology, Nanjing University, China
Jian Lu , State Key Laboratory for Novel Software Technology, Nanjing University, China
Community Question Answering (CQA) sites have become valuable repositories that host a massive volume of human knowledge. How can we detect a high-value answer which clears the doubts of many users? Can we tell the user if the question s/he is posting would attract a good answer? In this paper, we aim to answer these questions from the perspective of the voting outcome by the site users. Our key observation is that the voting score of an answer is strongly positively correlated with that of its question, and such correlation could be in turn used to boost the prediction performance. Armed with this observation, we propose a family of algorithms to jointly predict the voting scores of questions and answers soon after they are posted in the CQA sites. Experimental evaluations demonstrate the effectiveness of our approaches.
Correlation, Joints, Knowledge discovery, Prediction algorithms, Educational institutions, Logistics, Conferences
Y. Yao, H. Tong, T. Xie, L. Akoglu, F. Xu and J. Lu, "Joint voting prediction for questions and answers in CQA," 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), China, 2014, pp. 340-343.