Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
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
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
ITR: A Case-Based Travel Advisory System
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
More than the sum of its members: challenges for group recommender systems
Proceedings of the working conference on Advanced visual interfaces
Two methods for enhancing mutual awareness in a group recommender system
Proceedings of the working conference on Advanced visual interfaces
IEEE Transactions on Knowledge and Data Engineering
Personalization technologies: a process-oriented perspective
Communications of the ACM - The digital society
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
Collaborative recommender systems: Combining effectiveness and efficiency
Expert Systems with Applications: An International Journal
Optimizing Preferences within Groups: A Case Study on Travel Recommendation
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Managing uncertainty in group recommending processes
User Modeling and User-Adapted Interaction
Personalized Recommendation over a Customer Network for Ubiquitous Shopping
IEEE Transactions on Services Computing
A Group Recommender System for Tourist Activities
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Exploiting Domain Knowledge by Automated Taxonomy Generation in Recommender Systems
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
Knowledge infusion into content-based recommender systems
Proceedings of the third ACM conference on Recommender systems
Preference aggregation in group recommender systems for committee decision-making
Proceedings of the third ACM conference on Recommender systems
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
A Scalable, Accurate Hybrid Recommender System
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations
Information Sciences: an International Journal
Information Sciences: an International Journal
Enhanced vector space models for content-based recommender systems
Proceedings of the fourth ACM conference on Recommender systems
On the design of individual and group recommender systems for tourism
Expert Systems with Applications: An International Journal
News personalization using the CF-IDF semantic recommender
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Information Sciences: an International Journal
Hybrid voting protocols and hardness of manipulation
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
Generating recommendations for consensus negotiation in group personalization services
Personal and Ubiquitous Computing
Context-Aware Multi-Agent Planning in intelligent environments
Information Sciences: an International Journal
A negotiation framework for heterogeneous group recommendation
Expert Systems with Applications: An International Journal
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A key issue in group recommendation is how to combine the individual preferences of different users that form a group and elicit a profile that accurately reflects the tastes of all members in the group. Most Group Recommender Systems (GRSs) make use of some sort of method for aggregating the preference models of individual users to elicit a recommendation that is satisfactory for the whole group. In general, most GRSs offer good results, but each of them have only been tested in one application domain. This paper describes a domain-independent GRS that has been used in two different application domains. In order to create the group preference model, we select two techniques that are widely used in other GRSs and we compare them with two novel techniques. Our aim is to come up with a model that weighs the preferences of all the individuals to the same extent in such a way that no member in the group is particularly satisfied or dissatisfied with the final recommendations.