Algorithms for clustering data
Algorithms for clustering data
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
Improving Accuracy of Recommender System by Item Clustering
IEICE - Transactions on Information and Systems
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Dynamic fuzzy clustering for recommender systems
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Stochastic search for global neighbors selection in collaborative filtering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
From neighbors to global neighbors in collaborative filtering: an evolutionary optimization approach
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A comparison of clustering-based privacy-preserving collaborative filtering schemes
Applied Soft Computing
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It is on human nature to seek for recommendation before any purchase or service request. This trend increases along with the enormous information, products and services evolution, and becomes more and more challenging to create robust, and scalable recommender systems that are able to perform in real time. A popular approach for increasing the scalability and decreasing the time complexity of recommender systems, involves user clustering, based on their profiles and similarities. Cluster representatives make successful recommendations for the other cluster members; this way the complexity of recommendation depends only on cluster size. Although classic clustering methods have been often used, the requirements of user clustering in recommender systems, are quite different from the typical ones. In particular, there is no reason to create disjoint clusters or to enforce the partitioning of all the data. In order to eliminate these issues we propose a data clustering method that is based on genetic algorithms. We show, based on findings, that this method is faster and more accurate than classic clustering schemes. The use of clusters created, based on the proposed method, leads to significantly better recommendation quality.