dc.creatorDe Castro P.A.D.
dc.creatorDe Franca F.O.
dc.creatorFerreira H.M.
dc.creatorVon Zuben F.J.
dc.date2007
dc.date2015-06-30T18:39:12Z
dc.date2015-11-26T14:30:39Z
dc.date2015-06-30T18:39:12Z
dc.date2015-11-26T14:30:39Z
dc.date.accessioned2018-03-28T21:34:00Z
dc.date.available2018-03-28T21:34:00Z
dc.identifier0769529461; 9780769529462
dc.identifierProceedings - 7th International Conference On Hybrid Intelligent Systems, His 2007. , v. , n. , p. 65 - 70, 2007.
dc.identifier
dc.identifier10.1109/ICHIS.2007.4344029
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-47149117785&partnerID=40&md5=c99db6eca3ad89746058ddfa3bdaa1a1
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/104172
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/104172
dc.identifier2-s2.0-47149117785
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1247171
dc.descriptionCollaborative 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.
dc.description
dc.description
dc.description65
dc.description70
dc.descriptionAgrawal, 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. 94-105
dc.descriptionCheng, Y., Church, G.M., Biclustering of expression data (2000) Proc. of the 8th Int. Conf. on Intelligent Systems for Molecular Biology, pp. 93-103
dc.descriptionCastro, 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, , Brazil
dc.descriptionCastro, 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. , Brazil
dc.descriptionDhillon, 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. 269-274
dc.descriptionGoldberg, D., Nichols, D., Brian, M., Terry, D., Using collaborative filtering to weave an information tapestry (1992) ACM Communicat, 35 (12), pp. 61-70
dc.descriptionHaixun, 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. 394-405
dc.descriptionHartigan, 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, eds
dc.descriptionMadeira, S.C., Oliveira, A.L., (2004) Biclustering algorithms for biological data analysis: A survey, 1 (24-25). , Trans. on Computational Biology and Bioinformatics
dc.descriptionResnick, 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. 175-186
dc.descriptionSheng, Q., Moreau, Y., De Moor, B., Biclustering microarray data by Gibbs sampling (2003) Bioinformatics, 19 (SUPPL. 2), pp. 196-205
dc.descriptionSymeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y., Nearest-Biclusters Collaborative Filtering (2006) Proc. of the WebKDD - Workshop held in conjunction with KDD
dc.descriptionYu, 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. 56-59
dc.descriptionMiyahara, K., Pazzani, M.J., Collaborative Filtering with the simple Bayesian Classifier (2000) Proc. of the 6th Pacific Rim International Conference on Artificial Intelligence, pp. 679-689
dc.descriptionHerlocker, 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. 230-237
dc.descriptionGoldberg, K., Roeder, T., Gupta, D., Perkins, C., Eigentaste: A constant time collaborative filtering algorithm (2001) Information Retrieval, 4 (2), pp. 133-151
dc.descriptionBrozovsky, L., Petricek, V., Recommender System for Online Dating Service (2007) Proc. Of Conference Znalosti
dc.languageen
dc.publisher
dc.relationProceedings - 7th International Conference on Hybrid Intelligent Systems, HIS 2007
dc.rightsfechado
dc.sourceScopus
dc.titleEvaluating The Performance Of A Biclustering Algorithm Applied To Collaborative Filtering - A Comparative Analysis
dc.typeActas de congresos


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