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

Jung Yi Jiang, Lian Wang Lee, Shie Jue Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages2875-2880
Number of pages6
DOIs
Publication statusPublished - 2010 Nov 15
Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
Duration: 2010 Jul 112010 Jul 14

Publication series

Name2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume6

Other

Other2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
CountryChina
CityQingdao
Period10-07-1110-07-14

All Science Journal Classification (ASJC) codes

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

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