Analysis and Classification of Multi-Criteria Recommender Systems |
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Authors: | Nikos Manouselis Constantina Costopoulou |
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Affiliation: | (1) Informatics Laboratory, Division of Informatics, Mathematics & Statistics, Department of Science, Agricultural University of Athens, 75 Iera Odos str., 118 55 Athens, Greece |
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Abstract: | Recent studies have indicated that the application of Multi-Criteria Decision Making (MCDM) methods in recommender systems
has yet to be systematically explored. This observation partially contradicts with the fact that in related literature, there
exist several contributions describing recommender systems that engage some MCDM method. Such systems, which we refer to as
multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommendation,
in numerous application domains. On the other hand, a comprehensive analysis of existing systems would facilitate their understanding
and development. Towards this direction, this paper identifies a set of dimensions that distinguish, describe and categorize
multi-criteria recommender systems, based on existing taxonomies and categorizations. These dimensions are integrated into
an overall framework that is used for the analysis and classification of a sample of existing multi-criteria recommender systems.
The results provide a comprehensive overview of the ways current multi-criteria recommender systems support the decision of
online users. |
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Keywords: | recommender systems Multi-Criteria Decision Making (MCDM) classification |
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