A Multi-Objective Multipopulation Approach for Biclustering
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Improving a multi-objective multipopulation artificial immune network for biclustering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Query expansion using an immune-inspired biclustering algorithm
Natural Computing: an international journal
Predicting missing values with biclustering: A coherence-based approach
Pattern Recognition
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Collaborative filtering (CF) is a method to perform automated suggestions for a user based on the opinion of other users with similar interest. Most of the CF algorithms do not take into account the existent duality between users and items, considering only the similarities between users or only the similarities between items. The authors have proposed in a previous work a bio-inspired methodology for CF, namely BIC-aiNet, capable of clustering rows and columns of a data matrix simultaneously. The usefulness and performance of the methodology are reported in the literature. Now, the authors carry out more rigorous comparative experiments with BICaiNet and other techniques found in the literature, as well as evaluate the scalability of the algorithm in several datasets of different sizes. The results indicate that our proposal is able to provide useful recommendations for the users, outperforming other methodologies for CF.