MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Let's browse: a collaborative Web browsing agent
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Flytrap: intelligent group music recommendation
Proceedings of the 7th international conference on Intelligent user interfaces
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
More than the sum of its members: challenges for group recommender systems
Proceedings of the working conference on Advanced visual interfaces
An Accurate and Scalable Collaborative Recommender
Artificial Intelligence Review
Proceedings of the 10th international conference on Intelligent user interfaces
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
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
A Case-Based Song Scheduler for Group Customised Radio
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Prototyping recommender systems in jcolibri
Proceedings of the 2008 ACM conference on Recommender systems
Key figure impact in trust-enhanced recommender systems
AI Communications - Recommender Systems
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
Personality aware recommendations to groups
Proceedings of the third ACM conference on Recommender systems
Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
Detecting professional versus personal closeness using an enterprise social network site
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative filtering recommender systems
The adaptive web
The adaptive web
Group-based recipe recommendations: analysis of data aggregation strategies
Proceedings of the fourth ACM conference on Recommender systems
Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
The needs of the many: a case-based group recommender system
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Combining provenance with trust in social networks for semantic web content filtering
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
A group recommendation system for online communities
International Journal of Information Management: The Journal for Information Professionals
A case-based solution to the cold-start problem in group recommenders
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this article we review the existing techniques in group recommender systems and we propose some improvement based on the study of the different individual behaviors when carrying out a decision-making process. Our method includes an analysis of group personality composition and trust between each group member to improve the accuracy of group recommenders. This way we simulate the argumentation process followed by groups of people when agreeing on a common activity in a more realistic way. Moreover, we reflect how they expect the system to behave in a long term recommendation process. This is achieved by including a memory of past recommendations that increases the satisfaction of users whose preferences have not been taken into account in previous recommendations.