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Adapting News and Advertisements to Groups

  • Berardina De Carolis
Chapter
Part of the Human-Computer Interaction Series book series (HCIS)

Abstract

This chapter deals with adaptation of background information and ­advertisements, displayed in an environment, to the interests of the group of people present. According to research on computational advertising, it is important to develop methods for finding the “best match” between user interests in a given context and available advertisements. Accordingly, after providing an overview of the most popular group recommender approaches, this chapter looks at new issues that arise when considering group modeling in pervasive advertising conveyed through digital displays. The chapter first discusses general issues concerning group recommender systems, with particular emphasis on the acquisition of user preferences and interests. A system called GAIN (Group Adaptive Information and News) is then presented. This was developed with the aim of tailoring the display of background information and advertisements to groups of people.

Keywords

Group Modeling Recommender System Activity Zone Time Slice Group Preference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The author is grateful to Dr. Brian Bloch for his comprehensive editing of the manuscript.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of BariBariItaly

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