Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
Improved neighborhood-based algorithms for large-scale recommender systems
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Hybrid web recommender systems
The adaptive web
Multicriteria User Modeling in Recommender Systems
IEEE Intelligent Systems
Multi-criteria service recommendation based on user criteria preferences
Proceedings of the fifth ACM conference on Recommender systems
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Accuracy improvements for multi-criteria recommender systems
Proceedings of the 13th ACM Conference on Electronic Commerce
IEEE Transactions on Consumer Electronics
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The Multi-Criteria Recommender systems continue to be interesting and challenging problem. In this paper we will propose an approach for selection of relevant items in a RS based on multi-criteria ratings and a method of computing weights of criteria taken from Multi-criteria Decision Making (MCDM). This method proposes a correlation coefficient and standard deviation integrated approach for determining weight of criteria in multi-criteria recommender systems. We evaluated the proposed method on an example of movies recommendation. Our approach was compared to some other metrics used in Information Theoretic approach to illustrate its potential applications.