Expert Detection In Question Answer Communities

Abstract

Community Question Answering has a crucial role in almost all societies nowadays. It is important for the owners of a community to be able to make it better and more reliable. One way to achieve this, is to find the users who have more knowledge, expertise, experience and skill and can well share their knowledge with others (which we call experts and aim to encourage them to be more active in the website). One method to use is to identify expert users, and whenever a new question is asked, we suggest this question to them to check and answer if its in their area of expertise. One way to encourage users to post replies, is to use gameplay techniques such as assigning points and badges to users. But as we will discuss, this method does not always detect expert users well, because some users will try to have small and insignificant but numerous activities that will make them gain a lot of points, however they are not experts. In this study, we examine the methods by which experts in a question-and-answer system can be found, and try to evaluate and compare these methods, use their ideas and positive points, and add our own new ideas to a new way of finding them. We used some ideas such as profile making for users, categorize users' expertise, A-Priori algorithm and showed that neural networks method results the best for the purpose of expert detection.