Analysis and Classification of Multi-Criteria Recommender Systems

@article{citeulike:1784660, abstract = {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.}, author = {Manouselis, Nikos and Costopoulou, Constantina }, citeulike-article-id = {1784660}, doi = {http://dx.doi.org/10.1007/s11280-007-0019-8}, journal = {World Wide Web}, month = {December}, number = {4}, pages = {415--441}, posted-at = {2007-10-23 08:40:52}, priority = {5}, title = {Analysis and Classification of Multi-Criteria Recommender Systems}, url = {http://dx.doi.org/10.1007/s11280-007-0019-8}, volume = {10}, year = {2007} }

TY - JOUR ID - citeulike:1784660 L3 - citeulike-article-id:1784660 TI - Analysis and Classification of Multi-Criteria Recommender Systems JF - World Wide Web VL - 10 IS - 4 SP - 415 EP - 441 N2 - 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. AU - Manouselis, Nikos AU - Costopoulou, Constantina PY - 2007/12/04/ UR - http://dx.doi.org/10.1007/s11280-007-0019-8 ER -