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Aproximando a los sistemas recomendadores desde los algoritmos genéticos

dc.contributor.authorVélez Langs, Oswaldospa
dc.contributor.authorSantos, Carlosspa
dc.date.accessioned2020-10-27T00:21:02Z
dc.date.available2020-10-27T00:21:02Z
dc.date.issued2006-12-01
dc.identifier.issn2539-2115
dc.identifier.issn1657-2831
dc.identifier.urihttp://hdl.handle.net/20.500.12749/9004
dc.description.abstractEl presente trabajo abarca un enfoque alternativo, desde los algoritmos evolutivos, a la manera tradicional en que se abordan los sistemas recomendadores (SR de aquí en adelante). Se examinan las posibilidades de los algoritmos genéticos para brindar características adaptativas a estos sistemas. Nuestro objetivo, además de proporcionar una panorámica informativa general sobre las posibilidades y potencialidades de los SR, es proveer mecanismos para que los SR sean capaces de aprender características personales desde los usuarios, con miras a mejorar la efectividad a la hora de encontrar recomendaciones y sugerencias apropiadas para un individuo en particular.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherUniversidad Autónoma de Bucaramanga UNAB
dc.relationhttps://revistas.unab.edu.co/index.php/rcc/article/view/1047/1020
dc.relation.urihttps://revistas.unab.edu.co/index.php/rcc/article/view/1047
dc.relation.urihttp://hdl.handle.net/20.500.12749/20387spa
dc.rightsDerechos de autor 2006 Revista Colombiana de Computación
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.sourceRevista Colombiana de Computación; Vol. 7 Núm. 2 (2006): Revista Colombiana de Computación; 7-23
dc.subjectCiencia de los computadores
dc.subjectIngeniería de sistemas
dc.subjectInvestigaciones
dc.subjectTecnologías de la información y las comunicaciones
dc.subjectTIC´s
dc.titleAproximando a los sistemas recomendadores desde los algoritmos genéticos
dc.title.translatedApproaching recommender systems from genetic algorithmseng
dc.publisher.facultyFacultad Ingeniería
dc.publisher.programPregrado Ingeniería de Sistemas
dc.type.driverinfo:eu-repo/semantics/article
dc.type.localArtículospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsTechnological innovationseng
dc.subject.keywordsComputer scienceeng
dc.subject.keywordsTechnology developmenteng
dc.subject.keywordsSystems engineeringeng
dc.subject.keywordsInvestigationseng
dc.subject.keywordsInformation and communication technologieseng
dc.subject.keywordsICT'seng
dc.subject.keywordsCollaborative information filtering
dc.subject.keywordsMachine learning
dc.subject.keywordsEvolutionary algorithms
dc.subject.keywordsAdaptive user interfaces
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNABspa
dc.type.hasversionInfo:eu-repo/semantics/publishedVersion
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.relation.referencesAggarwal, Ch. C., Wolf, J. L., Wu, K-L., and Yu, P. S. Horting hatches an egg: A new graph-theoretic approach to collaborative fi ltering. In Knowledge Discovery and Data Mining, 1999. pp. 201-212
dc.relation.referencesBelew, R. K. Adaptive information retrieval. In Proceedings of the Twelfth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, , June 1989, Cambridge, MA,. pp 11-20
dc.relation.referencesBreese, J.S., Heckerman, D. and Kadie, C. Empirical analysis of predictive algorithms for collaborative fi ltering. In Proceedings of the 14th Conference on Uncertainty in Artifi cial Intelligence 1998. pp. 43-52
dc.relation.referencesChristakou, C., Stafylopatis, A. A hybrid movie recommender system based on neural networks. In Proceedings 5th International Conference on Intelligent Systems Design and Applications, 2005. ISDA ‘05., Sept. 2005, pp 500 – 505
dc.relation.referencesCleverdon, C., Mills, J., Keen, M. Factors Determining the Performance of Indexing Systems , Vol. 2--Test Results. ASLIB Cranfi eld Res. Proj., Cranfi eld, Bedford, England, 1966.
dc.relation.referencesGeyer-Schulz, A., Hahsler, M., Jahn, M. myVU: A Next Generation Recommender System Based on Observed Consumer Behavior and Interactive Evolutionary Algorithms. In: W. Gaul, O. Opitz, M. Schader (Eds.): Data Analysis – Scientifi c Modeling and Practical Applications, Studies in Classifi cation, Data Analysis, and Knowledge Organization, Vol. 18, 2000. Springer, Heidelberg, 447-457
dc.relation.referencesHeckerman, D., Chickering, D., Meek, C., Rounthwaite, R., Kadie, C. Dependency Networks for Density Estimation, Collaborative Filtering, and Data Visualization. Journal of Machine Learn-ing Research. 1:49-75, 2000
dc.relation.referencesHerlocker, J.L., Konstan, J.A., Borchers, A. and Riedl, J.. An Algorithmic Framework for Per-forming Collaborative Filtering. In SIGIR ’99: proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230-237, 1999.
dc.relation.referencesKwok, K. L A neural network for probablistic information retrieval. In Proceedings of the Twelfth Annual nternational ACM/SIGIR Conference on Research and Development in Informa-tion Retrieval, , June 1989, Cambridge, MA,. pp 21-30
dc.relation.referencesMalone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A. and Cohen, M.D. Intelligent information sharing systems, -Communications of the ACM, 30(5) 1987, 390-402.
dc.relation.referencesMin Tjoa, A., Höfferer, M., Ehrentraut, G., Untersmayr, P. Applying Evolutionary Algorithms to the Problem of Information Filtering. DEXA Workshop 1997: 450-458
dc.relation.referencesMoukas, A., Maes., P. Amalthaea: an evolving multi-agent information fi ltering and discovery system for the WWW. Autonomous Agents and Multi-agent Systems, 1(1) 1998, pp 59-88.
dc.relation.referencesNasraoui, O., and Pavuluri, M. Accurate Web Recommendations Based on Profi le-Specifi c URL-Predictor Neural Networks. In Proceedings of the International World Wide Web Conference, New York, NY, May. 2004
dc.relation.referencesNichols, D. M. Implicit Rating and Filtering. In Proceedings of the Fifth DELOS Workshop on Filtering and Collaborative Filtering, Nov. 1997, ERCIM: pp.31-36
dc.relation.referencesSalton, G., and McGill, M.J. Introduction to Modern Information Retrieval. McGraw-Hill, New York, 1983
dc.relation.referencesSarwar, B., Karypis, G., Konstan, J. and J. Riedl. Analysis of recommendation algorithms for e-commerce. In Proceedings of ACM E-Commerce, 2000
dc.relation.referencesSebastiani, F. Machine Learning in Automated Text Categorisation. Technical Report IEIB4-31-1999, Consiglio Nazionale delle Ricerche, Pisa, Italy, 1999
dc.relation.referencesSheth, B., Maes, P. Evolving agents for personalized information fi ltering. In Proc on Artifi cial Intelligence for Applications 1993. US, 345-352
dc.relation.referencesUjjin, S. and Bentley, P. J. Learning User Preferences Using Evolution. In Proceedings of the 4th Asia-Pacifi c Conference on Simulated Evolution And Learning (SEAL’02) 2002. Singapore.
dc.relation.referencesUngar, l., Foster, D. Clustering Methods for Collaborative Filtering (1998). Proceedings of the Workshop on Recommendation Systems
dc.contributor.cvlacVélez Langs, Oswaldo [0000282073]
dc.subject.lembInnovaciones tecnológicas
dc.subject.lembDesarrollo de tecnología
dc.identifier.repourlrepourl:https://repository.unab.edu.co
dc.description.abstractenglishThis work presents an alternative approach (Evolutionary Algorithms approach) to traditional treatment of Recommender Systems (RSs). The work examines genetic algorithms possibilities to offer adaptive characteristics to this systems trough learning. The main goal, in addition to give a general view about RSs capabilities and possibilities, it is to provide an example mechanism for to extend RSs learning capabilities (from users ́s personal chracteristics), with the purpose to improve the effectiveness in the moment of to fi nd recommendations and appropriate suggestions for particular individuals.eng
dc.subject.proposalFiltrado colaborativo de la Información
dc.subject.proposalAprendizaje automático
dc.subject.proposalAlgoritmos evolutivos
dc.subject.proposalInterfaces de usuario adaptivas
dc.type.redcolhttp://purl.org/redcol/resource_type/CJournalArticle
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*


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