A Regularized Competition Model for Question Difficulty Estimation in Community Question Answering Services

Estimating questions ’ difficulty levels is an important task in community question answering (CQA) services. Previous stud-ies propose to solve this problem based on the question-user comparisons extract-ed from the question answering threads. However, they suffer from data sparseness problem as each question only gets a lim-ited number of comparisons. Moreover, they cannot handle newly posted question-s which get no comparisons. In this pa-per, we propose a novel question difficul-ty estimation approach called Regularized Competition Model (RCM), which natu-rally combines question-user comparisons and questions ’ textual descriptions into a unified framework. By incorporating tex-tual information, RCM can effectively deal with data sparseness problem. We further employ a K-Nearest Neighbor approach to estimate difficulty levels of newly post-ed questions, again by leveraging textu-al similarities. Experiments on two pub-licly available data sets show that for both well-resolved and newly-posted question-s, RCM performs the estimation task sig-nificantly better than existing methods, demonstrating the advantage of incorpo-rating textual information. More interest-ingly, we observe that RCMmight provide an automatic way to quantitatively mea-sure the knowledge levels of words.