Group Non-negative Matrix Factorization with Natural Categories for Question Retrieval in Community Question Answer Archives

Community question answering (CQA) has become an important service due to the popularity of CQA archives on the web. A distinctive feature is that CQA services usually organize questions into a hierarchy of natural categories. In this paper, we focus on the problem of question retrieval and propose a novel approach, called group non-negative matrix factorization with natural categories (GNMFNC). This is achieved by learning the category-specific topics for each category as well as shared topics across all categories via a group non-negative matrix factorization framework. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. Experiments are carried out on a real world CQA data set from Yahoo! Answers. The results show that our proposed approach significantly outperforms various baseline methods and achieves the state-of-the-art performance for question retrieval.