MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Variable Sociability in Agent-Based Decision Making
ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Personalisation for user agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Group recommender systems: a critiquing based approach
Proceedings of the 11th international conference on Intelligent user interfaces
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 group recommendation system with consideration of interactions among group members
Expert Systems with Applications: An International Journal
Group Recommending: A methodological Approach based on Bayesian Networks
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Content-based recommendation systems
The adaptive web
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The problem of building Recommender Systems has attracted considerable attention in recent years, but most recommender systems are designed for recommending items for individuals. In this paper we develop a content based group recommender system that can recommend TV shows to a group of users. We propose a method that uses decision list rule learner (DLRL) based on Ripper to learn the rule base from user viewing history and a method called RTL strategy based on social choice theory strategies to generate group ratings. We compare our learning algorithm with the existing C4.5 rule learner and the experimental results show that the performance of our rule learner is better in terms of literals learned (size of the rule set) and our rule learner takes time that is linear to the number of training examples.