Recommending QA Documents for Communities of Question-Answering Websites

Abstract

Question & Answering (Q&A) websites have become an essential knowledge-sharing platform. This platform provides knowledge-community services where users with common interests or expertise can form a knowledge community to collect and share QA documents. However, due to the massive amount of QAs, information overload can become a major problem. Consequently, a recommendation mechanism is needed to recommend QAs for communities of Q&A websites. Existing studies did not investigate the recommendation mechanisms for knowledge collections in communities of Q&A Websites. In this work, we propose a novel recommendation method to recommend related QAs for communities of Q&A websites. The proposed method recommends QAs by considering the community members\' reputations, the push scores and collection time of QAs, the complementary relationships between QAs and their relevance to the communities. Experimental results show that the proposed method outperforms other conventional methods, providing a more effective manner to recommend QA documents to knowledge communities.