IASK: A Distributed Q&A System Incorporating Social Community and Global Collective Intelligence

Abstract

Traditional web-based Question and Answer (Q&A) websites cannot easily solve non-factual questions to match askers’ preference. Recent research efforts begin to study social-based Q&A systems that rely on an asker's social friends to provide answers. However, this method cannot find answerers for a question not belonging to the asker's interests. To solve this problem, we propose a distributed Q&A system incorporating both social community intelligence and global collective intelligence, named as iASK. iASK improves the response latency and answer quality in both the social domain and global domain. It uses a neural network based friend ranking method to identify answerer candidates by considering social closeness and Q&A activities. To efficiently identify answerers in the global user base, iASK builds a virtual server tree that embeds the hierarchical structure of interests, and also maps users to the tree based on user interests. To accurately locate the cooperative experts, iASK has a fine-grained reputation system to evaluate user reputation based on their cooperativeness and expertise, and uses a reputation based reward strategy to encourage users to be cooperative. To further improve the performance of iASK, we propose a weak tie assisted social based potential answerer location algorithm and an interest coefficient based uncategorized question forwarding algorithm. To further improve the response quality and cooperativeness, we propose a reputation based reward strategy that motivates users to answer questions from unknown users. Experimental results from large-scale trace-driven simulation and real-world daily usages of the iASK prototype show the superior performance of iASK. It achieves high answer quality with 24 percent higher accuracy, short response latency with 53 percent less delay and effective cooperative incentives with 16 percent more answers compared to other social-based Q&A systems. The results also show the effectiveness of the enhancement algorithms in improving the performance of iASK.