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 phrasebased VSM. The results also show that the use of the MDL-based tree cut model is essential to our approach.
Question Recommendation Query Suggestion Tree Cut Model Minimum Description Length
Yunbo Cao Huizhong Duan Chin-Yew Lin Yong Yu Hsiao-Wuen Hon
Shanghai Jiao Tong University,Shanghai, China, 200240;Microsoft Research Asia,Beijing, China, 100080 Shanghai Jiao Tong University,Shanghai, China, 200240 Microsoft Research Asia,Beijing, China, 100080
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）