New Hybrid Recommender approaches: An application to equity funds selection

Abstract

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. © 2009 Springer-Verlag Berlin Heidelberg.