Static preference models for options with dynamic extent

Authors:
Thomas Bauereiß;Stefan Mandl;Bernd Ludwig
Affiliations:
Dept. of Computer Science 8, Artificial Intelligence, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;Dept. of Computer Science 8, Artificial Intelligence, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;Dept. of Computer Science 8, Artificial Intelligence, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Venue:
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Year:
2010

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Abstract

Models of user preferences are an important resource to improve the user experience of recommender systems. Using user feedback static preference models can be adapted over time. Still, if the options to choose from themselves have temporal extent, dynamic preferences have to be taken into account even when answering a single query. In this paper we propose that static preference models could be used in such situations by identifying an appropriate set of features.