MANAGING RESPONSIVE ENVIRONMENTS WITH SOFTWARE AGENTS
Applied Artificial Intelligence
Average and Majority Gates: Combining Information by Means of Bayesian Networks
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Managing uncertainty in group recommending processes
User Modeling and User-Adapted Interaction
Personality aware recommendations to groups
Proceedings of the third ACM conference on Recommender systems
Modelling a receiver's position to persuasive arguments
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Distributed deliberative recommender systems
Transactions on computational collective intelligence I
Layered evaluation of interactive adaptive systems: framework and formative methods
User Modeling and User-Adapted Interaction
Ambient intelligence: A survey
ACM Computing Surveys (CSUR)
Analysis of strategies for building group profiles
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Personalization in cultural heritage: the road travelled and the one ahead
User Modeling and User-Adapted Interaction
Informing the design of group recommender systems
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Towards effective group recommendations for microblogging users
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Evaluation of group profiling strategies
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Generating recommendations for consensus negotiation in group personalization services
Personal and Ubiquitous Computing
Social factors in group recommender systems
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Algorithms to Resolve Conflict in Multiuser Context Aware Ubiquitous Environment
International Journal of Advanced Pervasive and Ubiquitous Computing
A group recommender for movies based on content similarity and popularity
Information Processing and Management: an International Journal
Choosing which message to publish on social networks: a contextual bandit approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Including social factors in an argumentative model for Group Decision Support Systems
Decision Support Systems
Hybreed: A software framework for developing context-aware hybrid recommender systems
User Modeling and User-Adapted Interaction
Knowledge Elicitation Methods for Affect Modelling in Education
International Journal of Artificial Intelligence in Education
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This paper deals in depth with some of the emotions that play a role in a group recommender system, which recommends sequences of items to a group of users. First, it describes algorithms to model and predict the satisfaction experienced by individuals. Satisfaction is treated as an affective state. In particular, we model the decay of emotion over time and assimilation effects, where the affective state produced by previous items influences the impact on satisfaction of the next item. We compare the algorithms with each other, and investigate the effect of parameter values by comparing the algorithms' predictions with the results of an earlier empirical study. We discuss the difficulty of evaluating affective models, and present an experiment in a learning domain to show how some empirical evaluation can be done. Secondly, this paper proposes modifications to the algorithms to deal with the effect on an individual's satisfaction of that of others in the group. In particular, we model emotional contagion and conformity, and consider the impact of different relationship types. Thirdly, this paper explores the issue of privacy (feeling safe, not accidentally disclosing private tastes to others in the group) which is related to the emotion of embarrassment. It investigates the effect on privacy of different group aggregation strategies and proposes to add a virtual member to the group to further improve privacy.