Multi-criteria service recommendation based on user criteria preferences

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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. © 2011 ACM.
Original languageEnglish
Title of host publicationRecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems|RecSys - Proc. ACM Conf. Recomm. Syst.
Number of pages7
Publication statusPublished - 2011
Event5th ACM Conference on Recommender Systems, RecSys 2011 - Chicago, IL
Duration: 1 Jul 2011 → …


Conference5th ACM Conference on Recommender Systems, RecSys 2011
CityChicago, IL
Period1/07/11 → …


  • clustering
  • multi-criteria recommender systems
  • multiple criteria decision making
  • recommender systems
  • service


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