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
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Making Better Use of Global Discretization
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Decision trees for ordinal classification
Intelligent Data Analysis
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Recommender Systems and Multicriteria Decision Analysis remain two separate scientific fields in spite of their similarity in supporting the decision making process and reducing information overload. In this paper we present a novel algorithmic framework, which combines features from Recommender Systems literature and Multicriteria Decision Analysis to alleviate the sparsity problem and the absence of multidimensional correlation measures. We apply the introduced framework for recommending Greek equity funds to a set of simulation generated investors. The proposed framework treats MCDA's algorithm UTADIS as a content - based recommendation technique which, in conjunction with collaborative filtering results in two Hybrid Recommendation approaches. The resulting approaches manage to outperform the separate application of the UTADIS and collaborative filtering methods in terms of recommendation accuracy.