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  • 标题:Causal Analysis of User Search Query Intent
  • 本地全文:下载
  • 作者:Gahangir Hossain ; James Haarbauer ; Jonathan Abdo
  • 期刊名称:Journal of Computer and Communications
  • 印刷版ISSN:2327-5219
  • 电子版ISSN:2327-5227
  • 出版年度:2016
  • 卷号:04
  • 期号:14
  • 页码:108-131
  • DOI:10.4236/jcc.2016.414009
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要:We investigated the application of Causal Bayesian Networks (CBNs) to large data sets in order to predict user intent via internet search prediction. Here, sample data are taken from search engine logs (Excite, Altavista, and Alltheweb). These logs are parsed and sorted in order to create a data structure that was used to build a CBN. This network is used to predict the next term or terms that the user may be about to search (type). We looked at the application of CBNs, compared with Naive Bays and Bays Net classifiers on very large datasets. To simulate our proposed results, we took a small sample of search data logs to predict intentional query typing. Additionally, problems that arise with the use of such a data structure are addressed individually along with the solutions used and their prediction accuracy and sensitivity.
  • 关键词:Causal Bayesian Networks (CBNs);Query Search;Intervention;Reasoning;Inference Mechanisms;Prediction Methods
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