Finding and Ranking High-Quality Answers in Community Question Answering Sites

摘要

AbstractCommunity Question Answering (CQA) sites have become a very popular place to ask questions and give answers to a large community of users on the Internet. Stack Exchange is one of the popular CQA sites where a large amount of contents are posted every day in the form of questions, answers and comments. The answers on Stack Exchange are listed by their recent occurrences, time of posting or votes obtained by peer users under three tabs called active, oldest and votes, respectively. Votes tab is the default setting on the site and is also preferred tab of users because answers under this tab are voted as good answers by other users. The problem of voting-based sorting is that new answers which are yet to receive any vote are placed at the bottom in vote tab. The new answer may be of sufficiently high-quality to be placed at the top but no or fewer votes (later posting) have made them stay at the bottom. We introduce a new tab calledpromising answerstab where answers are listed based on their usefulness, which is calculated by our proposed system using the classification and regression models. Several textual features of answers and users reputation are used as features to predict the usefulness of the answers. The results are validated with good values of precision, recall, F1-score, area under the receiver operating characteristic curve (AUC) and root mean squared error. We also compare the top ten answers predicted by our system to the actual top ten answers based on votes and found that they are in high agreement.