A hybrid model to predict best answers in question answering communities

Abstract

Question answering communities (QAC) are nowadays becoming widely used due to the huge facilities and flow of information that it provides. These communities target is to share and exchange knowledge between users. Through asking and answering questions under large number of categories.Unfortunately there are a lot of issues existing that made knowledge process difficult. One of those issues is that not every asker has the knowledge and ability to select the best answer for his question, or even selecting the best answer based on subjective matters. Our analysis in this paper is conducted on stack overflow community. We proposed a hybrid model for predicting the best answer. The proposed model is consisting of two modules. The first module is the content feature which consists of three types of features; question-answer features, answer content features, and answer-answer features. In the second module we examine the use of non-content feature in predicting best answers by using novel reputation score function. Then we merge both of content and non-content features and use them in prediction. We conducted experiments to train three different classifiers using our new added features. The prediction accuracy is very promising.