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Elements of a Learning Interface for Genre Qualified Search

Andrea Stubbe, CIS, University of Munich
Christoph Ringlstetter
Randy Goebel

Even prior to content, the genre of a web document leads to a first coarse binary classification of the recall space in relevant and non-relevant documents. Thinking of a genre search engine, massive data will be available via explicit or implicit user feedback. These data can be used to improve and to customize the underlying classifiers. A taxonomy of user behaviors is applied to model different scenarios of information gain. Elements of such a learning interface, as for example the implications of the lingering time and the snippet genre recognition factor, are discussed.

Citation

A. Stubbe, C. Ringlstetter, R. Goebel. "Elements of a Learning Interface for Genre Qualified Search". Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, pp 791-797, December 2007.

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Category:	In Conference

BibTeX

@incollection{Stubbe+al:AustralianAI07,
  author = {Andrea Stubbe and Christoph Ringlstetter and Randy Goebel},
  title = {Elements of a Learning Interface for Genre Qualified Search},
  Pages = {791-797},
  booktitle = {Australian Joint Conference on Artificial Intelligence},
  year = 2007,
}

Last Updated: February 01, 2008
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Elements of a Learning Interface for Genre Qualified Search

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