Human-computer interaction
Information encountering: a conceptual framework for accidental information discovery
ISIC '96 Proceedings of an international conference on Information seeking in context
MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Tradeoffs in displaying peripheral information
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Interacting with embodied agents in public environments
Proceedings of the working conference on Advanced visual interfaces
IEEE Transactions on Knowledge and Data Engineering
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Patterns: service-oriented architecture and web services
Patterns: service-oriented architecture and web services
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In this paper we describe a system, called GAIN (Group Adapted Interaction for News), which selects background information to be displayed in public shared environments according to preferences of the group of people present in there. In ambient intelligence contexts, we cannot assume that the system will be able to know every users physically present in the environment and therefore to access to their profiles in order to compute the preferences of the entire group. For this reason, we assume that group members may be i) totally unknown, ii) completely or iii) partially known by the system. As we describe in the paper, in the first case, the system uses a group profile that is built statistically according to the results of a preliminary study. In the second case, the model of the group is created from the profiles of known users. In the third situation the group interests are modeled by integrating preferences of known members with a statistical prediction of the interests of unknown ones. Evaluation results proved that adapting news display to the group was more effective in matching the members' interests in all the three cases than the in the non-adaptive modality.