Study of the Temporal-Statistics-Based Reputation Models for Q&A Systems

Q&A systems are becoming a vital source of knowledge in many different domains. In some cases, they are also associated with services which provide employers with important information regarding the expertise of its potential employees. Therefore, the reputation earned in such communities can be associated with better job opportunities, and its significance is increasing. However, in a community where there is no direct financial motivation for participation, a reputation score is not solely an expertise metric. It is also a powerful motivator for remaining an active community member. Regardless of this complexity, algorithms for calculating reputation scores need to be as easy to understand (and implement) as possible. Therefore, the designers of the Q&A reputation system often implement a set of fixed rules, to some extent trading quality for quantity. Our goal is to study whether (and how) temporal statistics of a Q&A website can be incorporated into its reputation system. We want the proposed mechanism to dynamically adjust the impact of a single-answer evaluation on the reputation of its producer. We would like the proposed model to accurately reflect the expertise of content producers.