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.
In REF they developed group non-negative matrix factorization with learning the category-specific topics for each category as well as shared topics across all categories.
Group Non-negative Matrix Factorization with Natural Categories for Question Retrieval in Community Question Answer Archives
COLING
Computer Science