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CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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UTA-Rec: a recommender system based on multiple criteria analysis
Proceedings of the 2008 ACM conference on Recommender systems
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
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Statistics Without Maths for Psychology: Using Spss for Windows
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IEEE Intelligent Systems
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UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
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A group recommendation approach for service selection
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ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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Proceedings of International Conference on Information Integration and Web-based Applications & Services
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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Research in recommender systems is now starting to recognise the importance of multiple selection criteria to improve the recommendation output. In this paper, we present a novel approach to multi-criteria recommendation, based on the idea of clustering users in "preference lattices" (partial orders) according to their criteria preferences. We assume that some selection criteria for an item (product or a service) will dominate the overall ranking, and that these dominant criteria will be different for different users. Following this assumption, we cluster users based on their criteria preferences, creating a "preference lattice". The recommendation output for a user is then based on ratings by other users from the same or close clusters. Having introduced the general approach of clustering, we proceed to formulate three alternative recommendation methods instantiating the approach: (a) using the aggregation function of the criteria, (b) using the overall item ratings, and (c) combining clustering with collaborative filtering. We then evaluate the accuracy of the three methods using a set of experiments on a service ranking dataset, and compare them with a conventional collaborative filtering approach extended to cover multiple criteria. The results indicate that our third method, which combines clustering and extended collaborative filtering, produces the highest accuracy.