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 BIC-aiNet 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. © 2007 IEEE. 6570Agrawal, R., Gehrke, J., Gunopulus, D., Raghavan, P., Automatic subspace clustering of high dimensional data for data mining applications (1998) Proc. of the ACM/SIGMOD Int. Conference on Management of Data, pp. 94-105Cheng, Y., Church, G.M., Biclustering of expression data (2000) Proc. of the 8th Int. Conf. on Intelligent Systems for Molecular Biology, pp. 93-103Castro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J., Applying Biclustering to Perform Collaborative Filtering (2007) Proc. of the 7th Int. Conf. on Intelligent Systems Design and Applications, , BrazilCastro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J., Applying Biclustering to Text Mining: An immune-Inspired Approach (2007) Proc. of the 6th Int. Conf. on Artificial Immune Systems, pp. 83-94. , BrazilDhillon, I.S., Co-clustering documents and words using bipartite spectral graph partitioning (2001) Proc. of the 7th Int. Con. on Knowledge Discovery and Data Mining, pp. 269-274Goldberg, D., Nichols, D., Brian, M., Terry, D., Using collaborative filtering to weave an information tapestry (1992) ACM Communicat, 35 (12), pp. 61-70Haixun, W., Wei, W., Jiong, Y., Yu, P.S., Clustering by pattern similarity in large data sets (2002) Proc. of the 2002 ACM SIGMOD Int. Conference on Management of Data, pp. 394-405Hartigan, J. A, Direct clustering of a data matrix. Journal of the American Statistical Association (JASA), 1972, 67, no. 337, pp. 123-129Kim, M.W., Kim, E.J., Ryu, J.W., Collaborative Filtering for Recommendation Using Neural Networks (2005) Lecture Notes in Computer Science, 3480, pp. 127-136. , O. Gervasi et al, edsMadeira, S.C., Oliveira, A.L., (2004) Biclustering algorithms for biological data analysis: A survey, 1 (24-25). , Trans. on Computational Biology and BioinformaticsResnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J., Grouplens: An open architecture for collaborative filtering on netnews (1994) In Proc. of the Computer Supported Collaborative Work Conference, pp. 175-186Sheng, Q., Moreau, Y., De Moor, B., Biclustering microarray data by Gibbs sampling (2003) Bioinformatics, 19 (SUPPL. 2), pp. 196-205Symeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y., Nearest-Biclusters Collaborative Filtering (2006) Proc. of the WebKDD - Workshop held in conjunction with KDDYu, K., Schwaighofer, A., Tresp, V., Xu, X., Kriegel, H.-P., Probabilistic Memory-based Collaborative Filtering (2004) In IEEE Transactions on Knowledge and Data Engineering, pp. 56-59Miyahara, K., Pazzani, M.J., Collaborative Filtering with the simple Bayesian Classifier (2000) Proc. of the 6th Pacific Rim International Conference on Artificial Intelligence, pp. 679-689Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J., An algorithmic framework for performing collaborative filtering (1999) Proc. of the 22nd Annual international ACM SIGIR Conference on Research and Development in information Retrieval, pp. 230-237Goldberg, K., Roeder, T., Gupta, D., Perkins, C., Eigentaste: A constant time collaborative filtering algorithm (2001) Information Retrieval, 4 (2), pp. 133-151Brozovsky, L., Petricek, V., Recommender System for Online Dating Service (2007) Proc. Of Conference Znalosti