Aproximando a los sistemas recomendadores desde los algoritmos genéticos

El 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. Nue...

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Autores:: Vélez Langs, Oswaldo
Santos, Carlos

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2006

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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dc.title.none.fl_str_mv	Aproximando a los sistemas recomendadores desde los algoritmos genéticos
dc.title.translated.eng.fl_str_mv	Approaching recommender systems from genetic algorithms
title	Aproximando a los sistemas recomendadores desde los algoritmos genéticos
spellingShingle	Aproximando a los sistemas recomendadores desde los algoritmos genéticos Ciencia de los computadores Ingeniería de sistemas Investigaciones Tecnologías de la información y las comunicaciones TIC´s Technological innovations Computer science Technology development Systems engineering Investigations Information and communication technologies ICT's Collaborative information filtering Machine learning Evolutionary algorithms Adaptive user interfaces Innovaciones tecnológicas Desarrollo de tecnología Filtrado colaborativo de la Información Aprendizaje automático Algoritmos evolutivos Interfaces de usuario adaptivas
title_short	Aproximando a los sistemas recomendadores desde los algoritmos genéticos
title_full	Aproximando a los sistemas recomendadores desde los algoritmos genéticos
title_fullStr	Aproximando a los sistemas recomendadores desde los algoritmos genéticos
title_full_unstemmed	Aproximando a los sistemas recomendadores desde los algoritmos genéticos
title_sort	Aproximando a los sistemas recomendadores desde los algoritmos genéticos
dc.creator.fl_str_mv	Vélez Langs, Oswaldo Santos, Carlos
dc.contributor.author.spa.fl_str_mv	Vélez Langs, Oswaldo Santos, Carlos
dc.contributor.cvlac.none.fl_str_mv	Vélez Langs, Oswaldo [0000282073]
dc.subject.none.fl_str_mv	Ciencia de los computadores Ingeniería de sistemas Investigaciones Tecnologías de la información y las comunicaciones TIC´s
topic	Ciencia de los computadores Ingeniería de sistemas Investigaciones Tecnologías de la información y las comunicaciones TIC´s Technological innovations Computer science Technology development Systems engineering Investigations Information and communication technologies ICT's Collaborative information filtering Machine learning Evolutionary algorithms Adaptive user interfaces Innovaciones tecnológicas Desarrollo de tecnología Filtrado colaborativo de la Información Aprendizaje automático Algoritmos evolutivos Interfaces de usuario adaptivas
dc.subject.keywords.eng.fl_str_mv	Technological innovations Computer science Technology development Systems engineering Investigations Information and communication technologies ICT's
dc.subject.keywords.none.fl_str_mv	Collaborative information filtering Machine learning Evolutionary algorithms Adaptive user interfaces
dc.subject.lemb.none.fl_str_mv	Innovaciones tecnológicas Desarrollo de tecnología
dc.subject.proposal.none.fl_str_mv	Filtrado colaborativo de la Información Aprendizaje automático Algoritmos evolutivos Interfaces de usuario adaptivas
description	El 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.
publishDate	2006
dc.date.issued.none.fl_str_mv	2006-12-01
dc.date.accessioned.none.fl_str_mv	2020-10-27T00:21:02Z
dc.date.available.none.fl_str_mv	2020-10-27T00:21:02Z
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dc.relation.none.fl_str_mv	https://revistas.unab.edu.co/index.php/rcc/article/view/1047/1020
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dc.relation.uri.spa.fl_str_mv	http://hdl.handle.net/20.500.12749/20387
dc.relation.references.none.fl_str_mv	Aggarwal, 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 Belew, 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 Breese, 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 Christakou, 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 Cleverdon, 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. Geyer-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 Heckerman, 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 Herlocker, 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. Kwok, 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 Malone, 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. Min Tjoa, A., Höfferer, M., Ehrentraut, G., Untersmayr, P. Applying Evolutionary Algorithms to the Problem of Information Filtering. DEXA Workshop 1997: 450-458 Moukas, 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. Nasraoui, 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 Nichols, D. M. Implicit Rating and Filtering. In Proceedings of the Fifth DELOS Workshop on Filtering and Collaborative Filtering, Nov. 1997, ERCIM: pp.31-36 Salton, G., and McGill, M.J. Introduction to Modern Information Retrieval. McGraw-Hill, New York, 1983 Sarwar, B., Karypis, G., Konstan, J. and J. Riedl. Analysis of recommendation algorithms for e-commerce. In Proceedings of ACM E-Commerce, 2000 Sebastiani, F. Machine Learning in Automated Text Categorisation. Technical Report IEIB4-31-1999, Consiglio Nazionale delle Ricerche, Pisa, Italy, 1999 Sheth, B., Maes, P. Evolving agents for personalized information fi ltering. In Proc on Artifi cial Intelligence for Applications 1993. US, 345-352 Ujjin, 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. Ungar, l., Foster, D. Clustering Methods for Collaborative Filtering (1998). Proceedings of the Workshop on Recommendation Systems
dc.rights.none.fl_str_mv	Derechos de autor 2006 Revista Colombiana de Computación
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rights_invalid_str_mv	Derechos de autor 2006 Revista Colombiana de Computación http://creativecommons.org/licenses/by-nc-sa/4.0/ http://creativecommons.org/licenses/by-nc-nd/2.5/co/ Atribución-NoComercial-SinDerivadas 2.5 Colombia http://purl.org/coar/access_right/c_abf2
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dc.publisher.none.fl_str_mv	Universidad Autónoma de Bucaramanga UNAB
dc.publisher.faculty.none.fl_str_mv	Facultad Ingeniería
dc.publisher.program.none.fl_str_mv	Pregrado Ingeniería de Sistemas
publisher.none.fl_str_mv	Universidad Autónoma de Bucaramanga UNAB
dc.source.none.fl_str_mv	Revista Colombiana de Computación; Vol. 7 Núm. 2 (2006): Revista Colombiana de Computación; 7-23
institution	Universidad Autónoma de Bucaramanga - UNAB
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spelling	Vélez Langs, Oswaldo82ffea73-1f8d-4c08-b8c1-f15f01a60c8f-1Santos, Carlos38c2eab2-3e6e-4cc5-8808-3b7b8d3a4041-1Vélez Langs, Oswaldo [0000282073]2020-10-27T00:21:02Z2020-10-27T00:21:02Z2006-12-012539-21151657-2831http://hdl.handle.net/20.500.12749/9004instname:Universidad Autónoma de Bucaramanga UNABrepourl:https://repository.unab.edu.coEl 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.This 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.application/pdfspaUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaPregrado Ingeniería de Sistemashttps://revistas.unab.edu.co/index.php/rcc/article/view/1047/1020https://revistas.unab.edu.co/index.php/rcc/article/view/1047http://hdl.handle.net/20.500.12749/20387Aggarwal, 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. 201-212Belew, 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 11-20Breese, 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. 43-52Christakou, 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 – 505Cleverdon, 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.Geyer-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, 447-457Heckerman, 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, 2000Herlocker, 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.Kwok, 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 21-30Malone, 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.Min Tjoa, A., Höfferer, M., Ehrentraut, G., Untersmayr, P. Applying Evolutionary Algorithms to the Problem of Information Filtering. DEXA Workshop 1997: 450-458Moukas, 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.Nasraoui, 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. 2004Nichols, D. M. Implicit Rating and Filtering. In Proceedings of the Fifth DELOS Workshop on Filtering and Collaborative Filtering, Nov. 1997, ERCIM: pp.31-36Salton, G., and McGill, M.J. Introduction to Modern Information Retrieval. McGraw-Hill, New York, 1983Sarwar, B., Karypis, G., Konstan, J. and J. Riedl. Analysis of recommendation algorithms for e-commerce. In Proceedings of ACM E-Commerce, 2000Sebastiani, F. Machine Learning in Automated Text Categorisation. Technical Report IEIB4-31-1999, Consiglio Nazionale delle Ricerche, Pisa, Italy, 1999Sheth, B., Maes, P. Evolving agents for personalized information fi ltering. In Proc on Artifi cial Intelligence for Applications 1993. US, 345-352Ujjin, 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.Ungar, l., Foster, D. Clustering Methods for Collaborative Filtering (1998). Proceedings of the Workshop on Recommendation SystemsDerechos de autor 2006 Revista Colombiana de Computaciónhttp://creativecommons.org/licenses/by-nc-sa/4.0/http://creativecommons.org/licenses/by-nc-nd/2.5/co/Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Revista Colombiana de Computación; Vol. 7 Núm. 2 (2006): Revista Colombiana de Computación; 7-23Ciencia de los computadoresIngeniería de sistemasInvestigacionesTecnologías de la información y las comunicacionesTIC´sTechnological innovationsComputer scienceTechnology developmentSystems engineeringInvestigationsInformation and communication technologiesICT'sCollaborative information filteringMachine learningEvolutionary algorithmsAdaptive user interfacesInnovaciones tecnológicasDesarrollo de tecnologíaFiltrado colaborativo de la InformaciónAprendizaje automáticoAlgoritmos evolutivosInterfaces de usuario adaptivasAproximando a los sistemas recomendadores desde los algoritmos genéticosApproaching recommender systems from genetic algorithmsinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/redcol/resource_type/CJournalArticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85ORIGINAL2006_Aproximando a los sistemas recomendadores.pdf2006_Aproximando a los sistemas recomendadores.pdfArticuloapplication/pdf539957https://repository.unab.edu.co/bitstream/20.500.12749/9004/1/2006_Aproximando%20a%20los%20sistemas%20recomendadores.pdf94884c84819470ea18f99c64d170e3c3MD51open accessTHUMBNAIL2006_Aproximando a los sistemas recomendadores.pdf.jpg2006_Aproximando a los sistemas recomendadores.pdf.jpgIM Thumbnailimage/jpeg12308https://repository.unab.edu.co/bitstream/20.500.12749/9004/2/2006_Aproximando%20a%20los%20sistemas%20recomendadores.pdf.jpgbb642993c006807b362466ad53fde039MD52open access20.500.12749/9004oai:repository.unab.edu.co:20.500.12749/90042023-07-04 10:36:33.704open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.co

Aproximando a los sistemas recomendadores desde los algoritmos genéticos

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