User satisfaction in long term group recommendations
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Using personality to create alliances in group recommender systems
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Informing the design of group recommender systems
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Including social factors in an argumentative model for Group Decision Support Systems
Decision Support Systems
QA document recommendations for communities of question-answering websites
Knowledge-Based Systems
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In this paper we describe some new ideas to improve recommendations to groups of people. Our approach maximizes the global satisfaction for the group taking into account people personality and the social relationships among people in the group. We present some results with two cases of study based on the movie recommendation domain with heterogeneous groups. The first case study uses synthetically generated groups of people to test how the group composition affects the accuracy of the recommendation. Our second case study uses real users and groups where the topology of the groups is based on a social network. This second case of study with real users confirms the wide conclusions of the preliminary experiment with synthetic data, which allows us to conclude that it is possible to realize trustworthy experiments with synthetic data.