Fab: content-based, collaborative recommendation
Communications of the ACM
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ITR: A Case-Based Travel Advisory System
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Understanding and improving automated collaborative filtering systems
Understanding and improving automated collaborative filtering systems
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Collaborative filtering recommender systems
The adaptive web
The adaptive web
Adaptive bootstrapping of recommender systems using decision trees
Proceedings of the fourth ACM international conference on Web search and data mining
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
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In this paper we offer a potential solution to the cold-start problem in group recommender systems. To do so, we use information about previous group recommendation events and copy ratings from a user who played a similar role in some previous group event. We show that copying in this way, i.e. conditioned on groups, is superior to copying nothing and also superior to copying ratings from the most similar user known to the system.