Abstract
Social-network-based Question Answering (Q&A) systems are recently emanated due to their capabilities of outperforming classical search engines in answering non-factual questions. Social network users have different expertises and activity times, and identifying answerers with proper expertises, short response times, and high response rates is challenging for Q&A systems. To address this problem, we propose an optimal Q&A system that identifies answerers with required expertises and routes the questions with minimum possible response time in dynamic social networks. Our proposed system uses a hybrid model for estimating the expertise of each user, in order to identify the suitable answerers; besides, it avoids bottleneck answerers in the network, so as to increase the response rate. We conduct trace-driven simulations, which show that our Q&A system: (i) achieves up to 27% higher average response rate than the state-of-the-art systems, and (ii) reduces the average maximal response time by up to 60%. Moreover, the results show that, by varying the number of answerers, the number of keywords per question, the arrival rate of questions, and the predictability against the maximal response time, our Q&A system consistently outperforms the state-of-the-art systems.