Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
MUSICFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Understanding and Using Context
Personal and Ubiquitous Computing
Modelling and Optimisation Issues for Multidimensional Databases
CAiSE '00 Proceedings of the 12th International Conference on Advanced Information Systems Engineering
A Survey of Context-Aware Mobile Computing Research
A Survey of Context-Aware Mobile Computing Research
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
A data-oriented survey of context models
ACM SIGMOD Record
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
IEEE Transactions on Knowledge and Data Engineering
On Relaxing Contextual Preference Queries
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
And what can context do for data?
Communications of the ACM - Scratch Programming for All
Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
The adaptive web
Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
A survey on representation, composition and application of preferences in database systems
ACM Transactions on Database Systems (TODS)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
gRecs: a group recommendation system based on user clustering
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Fast group recommendations by applying user clustering
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
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Recommendation systems have received significant attention, with most of the proposed methods focusing on recommendations for single users. Recently, there are also approaches aiming at either group or context-aware recommendations. In this paper, we address the problem of contextual recommendations for groups. We exploit a hierarchical context model to extend a typical recommendation model to a general context-aware one that tackles the information needs of a group. We base the computation of contextual group recommendations on a subset of preferences of the users that present the most similar behavior to the group, that is, the users with the most similar preferences to the preferences of the group members, for a specific context. This subset of preferences includes the ones with context equal to or more general than the given context.