Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
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
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Making recommendations better: an analytic model for human-recommender interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
An information overload study: using design methods for understanding
OZCHI '06 Proceedings of the 18th Australia conference on Computer-Human Interaction: Design: Activities, Artefacts and Environments
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Applying relevant set correlation clustering to multi-criteria recommender systems
Proceedings of the third ACM conference on Recommender systems
Multi-criteria service recommendation based on user criteria preferences
Proceedings of the fifth ACM conference on Recommender systems
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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UTARec, a Recommender System that incorporates Multiple Criteria Analysis methodologies is presented. The system's performance and capability of addressing certain shortfalls of existing Recommender Systems is demonstrated in the case of movie recommendations. UTARec's accuracy is measured in terms of Kendall's tau and ROC curve analysis and is also compared to a Multiple Rating Collaborative Filtering (MRCF) approach. The results indicate that the proposed Multiple Criteria Analysis methodology can certainly improve the recommendation process by producing highly accurate results, from a user oriented perspective.