SmartQ: A Question and Answer System for Supplying High-Quality and Trustworthy Answers
Yu‐Hua Lin, Haiying Shen · IEEE Transactions on Big Data · 2017
Question and Answer (Q&A) systems aggregate the collected intelligence of all users to provide satisfying answers for questions. A well-developed Q&A system should incorporate features such as high question response rate, high answer quality, a spam-free environment for users and bridging disjoint social clusters. Previous works use reputation systems to achieve the goals. However, these reputation systems evaluate a user with an overall rating for all questions the user has answered regardless of the question categories, thus the reputation score does not accurately reflect the user's ability to answer a question in a specific category. We propose SmartQ: a reputation based Q&A System. SmartQ employs a category and theme based reputation management system to evaluate users' willingness and capability to answer various kinds of questions. The reputation system facilitates the forwarding of a question to favorable experts, which improves the question response rate and answer quality. SmartQ bridges disjoint social clusters by calculating reputation scores for each cluster on each question theme; SmartQ incorporates a lightweight spammer detection method to identify potential spammers. In order to reduce the loads of experts, we propose a strategy to recommend suggested answers from similar questions to each new question. Our trace-driven simulation on PeerSim demonstrates the effectiveness of SmartQ in providing good user experience. We then develop a real application of SmartQ and deploy it for use in a student group in Clemson University. The user feedback shows that SmartQ can provide high-quality answers for users in a community.