QA document recommendations for communities of question-answering websites

With the rapid development of Internet and Web 2.0 technology, 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. Community members can collect and share QA knowledge (documents) regarding their interests. However, due to the massive amount of QAs created every day, information overload can become a major problem. Consequently, a recommendation mechanism is needed to recommend QA documents for communities of Q&A websites. Existing studies did not investigate the recommendation mechanisms for knowledge collections in communities of Q&A Websites. Traditional recommendation methods use member importance as weight to consolidate individual profiles and generate group profiles, which in turn are used to filter out items of recommendation. However, they do not consider certain factors of the recommended items, such as the reputation of the community members and the complementary relationships between documents. In this work, we propose a novel method to recommend related QA documents for knowledge communities of Q&A websites. The proposed method recommends QA documents by considering factors such as the community members' reputation in collecting and answering QAs, the push scores and collection time of QAs, the complementary relationships between QAs and their relevance to the communities. This research evaluates and compares the proposed methods using an experimental dataset collected from Yahoo! Answers Taiwan website. Experimental results show that the proposed method outperforms other conventional methods, providing a more effective manner to recommend Q&A documents to knowledge communities.