A distributional similarity measure for query-dependent ranking in web mining

Jung Yi Jiang, Lian Wang Lee, Shie Jue Lee

研究成果: Conference contribution

摘要

Ranking model construction is an important topic in information retrieval and web mining. Recently, many approaches based on the idea of "learning to rank" have been proposed for this task and most of them attempt to score all documents of different queries by resorting to a single function. In this paper, we propose a distributional similarity measure for query-dependent ranking. In the query-dependent ranking framework, an individual ranking model is constructed for each training query with associated documents. When a new query is asked, the documents retrieved for the new query are ranked according to the scores determined by a joint ranking model which is combined from the individual models of similar training queries. The distributional similarity measure is used to calculate the similarities between queries. Experimental results show that our method is more effective than other approaches.

原文English
主出版物標題2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
頁面2875-2880
頁數6
DOIs
出版狀態Published - 2010 十一月 15
事件2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
持續時間: 2010 七月 112010 七月 14

出版系列

名字2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
6

Other

Other2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
國家China
城市Qingdao
期間10-07-1110-07-14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Human-Computer Interaction

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