Sequential pattern mining and belief revision for adaptive information retrieval

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 2005 International Conference on Active Media Technology, AMT 2005
Pages585-590
Volume2005
Publication statusPublished - 2005

Publication series

Name
Volume2005

Conference

Title2005 International Conference on Active Media Technology, AMT 2005
PlaceJapan
CityKagawa
Period19 - 21 May 2005

Abstract

Autonomous information agents alleviate the information overload problem on the Internet. The AGM belief revision framework provides a rigorous foundation to develop adaptive information agents. The expressive power of the belief revision logic allow a user's information preferences and contextual knowledge of a retrieval situation to be captured and reasoned about within a single logical framework. Contextual knowledge for information retrieval can be acquired via sequential pattern mining. This paper illustrates a novel approach of integrating the proposed data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. © 2005 IEEE.

Citation Format(s)

Sequential pattern mining and belief revision for adaptive information retrieval. / Lau, Raymond Y. K.; Li, Yuefeng.
Proceedings of the 2005 International Conference on Active Media Technology, AMT 2005. Vol. 2005 2005. p. 585-590 1505427.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review