document: Recommending questions using the mdl-based tree cut model The paper is concerned with the problem of question recommendation . Specifically , given a question as query , we are to retrieve and rank other questions according to their likelihood of being good recommendations of the queried question . A good recommendation provides alternative aspects around users ' interest . We tackle the problem of question recommendation in two steps : first represent questions as graphs of topic terms , and then rank recommendations on the basis of the graphs . We formalize both steps as the tree-cutting problems and then employ the MDL ( Minimum Description Length ) for selecting the best cuts . Experiments have been conducted with the real questions posted at Yahoo ! Answers . The questions are about two domains , ` travel ' and ` computers & internet ' . Experimental results indicate that the use of the MDL-based tree cut model can significantly outperform the baseline methods of word-based VSM or phrase-based VSM . The results also show that the use of the MDL-based tree cut model is essential to our approach .
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