Elements of a learning interface for genre qualified search

Abstract

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 me implications of me lingering time and the snippet genre recognition factor, are discussed. © Springer-Verlag Berlin Heidelberg 2007.