SISTEMAS DE RECOMENDACIÓN PARA LA TOMA DE DECISIONES. ESTADO DEL ARTE
SISTEMAS DE RECOMENDACIÓN PARA LA TOMA DE DECISIONES
DOI:
https://doi.org/10.47230/unesum-ciencias.v6.n1.2022.289Palabras clave:
Sistemas de Recomendación; estado del arte; filtrado colaborativo; vasado en contenido; basados en conocimiento.Resumen
Los Sistemas de Recomendación (SR) son técnicas de filtrado de información que nacen con el objetivo de facilitar o asistir al usuario en la toma de una decisión. El principal objetivo de estos sistemas es dar solución a problemas de sobrecarga de información, brindándole al usuario información sintetizada que pueda ser utilizada en la toma de decisiones. Las técnicas utilizadas para llevar a cabo las recomendaciones difieren unas de otras significativamente, tanto en la información requerida como en los procesos necesarios para llevar a cabo estas recomendaciones. La presente investigación tiene como objetivo realizar un estudio sobre el estado del arte actual de los SR. El estudio reveló que aunque los SR más utilizados y más conocidos son los colaborativos y los basados en contenido, no en todas las situaciones son los más adecuados.
Descargas
Citas
Aditya, P., Budi, I., & Munajat, Q. (2016). A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for E-commerce in Indonesia: A case study PT X. 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS),
Alshammari, M., Nasraoui, O., & Sanders, S. (2019). Mining Semantic Knowledge Graphs to Add Explainability to Black Box Recommender Systems. IEEE Access,7, 110563-110579. https://doi.org/10.1109/ACCESS.2019.2934633
Aprilianti, M., Mahendra, R., & Budi, I. (2016). Implementation of weighted parallel hybrid recommender systems for e-commerce in indonesia. 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS),
Barranco, M., Pérez, L., & Martínez, L. (2006). Un sistema de recomendación basado en conocimiento con información lingüística multigranular. In Proceedings of the SIGEF XIII: Optimization techniques: Fuzziness and nonlinearity for management and economy.
Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. Aaai/iaai,
Batyrshin, I. Z., & Sheremetov, L. B. (2006, 3-6 June 2006). Perception Based Associations in Time Series Data Bases. NAFIPS 2006 -2006 Annual Meeting of the North American Fuzzy Information Processing Society,
Bello, R., & Verdegay, J. L. (2010). Los conjuntos aproximados en el contexto de la Soft Computing. Revista Cubana de Ciencias Informáticas,4 pp. 5-24. .
Boran, F. E., Akay, D., & Yager, R. R. (2016). An overview of methods for linguistic summarization with fuzzy sets. Expert Systems with Applications,61, 356-377.
Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of library and information systems,69(Supplement 32), 175-186.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction,12(4), 331-370.
Castagnos, S., Jones, N., & Pu, P. (2010). Eye-tracking product recommenders' usage. Proceedings of the fourth ACM conference on Recommender systems,
Cornelio, O. M., Gulín, J. G., Fonseca, B. B., & Ching, I. S. Experiencia en la evaluación de competencias en un sistema de laboratorios a distancia. Anais do Encontro Virtual de Documentação em Software Livre e Congresso Internacional de Linguagem e Tecnologia Online,
Chen, C., Wang, D., & Ding, Y. (2016). User actions and timestamp based personalized recommendation for e-commerce system. 2016 IEEE International Conference on Computer and Information Technology (CIT),
Chen, L., & Pu, P. (2005). Trust building in recommender agents. Proceedings of the Workshop on Web Personalization, Recommender Systems and Intelligent User Interfaces at the 2nd International Conference on E-Business and Telecommunication Networks,
Cho, Y. S., & Ryu, K. H. (2008). Implementation of personalized recommendation system using demograpic data and RFM method in e-commerce. 2008 4th IEEE International Conference on Management of Innovation and Technology,
Dong, F., Luo, J., Zhu, X., Wang, Y., & Shen, J. (2013). A personalized hybrid recommendation system oriented to e-commerce mass data in the cloud. 2013 IEEE International Conference on Systems, Man, and Cybernetics,
Fan, Y., Shen, Y., & Mai, J. (2008). Study of the model of e-commerce personalized recommendation system based on data mining. 2008 International Symposium on Electronic Commerce and Security,
Finder, L. Intelligent user profiling using large-scale demographic data. Artificial Intelligence Magazine. v18 i2, 37-45.
Fonseca, B. B., Benítez, L. C. M., & Oliva, Á. M. H. (2019). La estructura de desglose del trabajo como mecanismo viable para la generación de proyectos exitosos. Serie Científica de la Universidad de las Ciencias Informáticas,12(5), 63-75.
Fonseca, B. B., Cornelio, O. M., & Marzo, F. R. R. (2020). Tratamiento de la incertidumbre en la evaluación del desempeño de los Recursos Humanos de un proyecto basado en conjuntos borrosos. Serie Científica de la Universidad de las CienciasInformáticas,13(6), 84-93.
Freire, J. B., Villagomez, M. M., Delgado, M. A. S., &Gónzalez, M. P. (2016). Modelo de recomendación de productos basado en computación con palabras y operadores OWA [A product recommendation model based on computing with word and OWA operators]. International Journal of Innovation and Applied Studies,16(1), 78.
Gaikwad, R. S., Udmale, S. S., & Sambhe, V. K. (2018). E-commerce recommendation system using improved probabilistic model. In Information and Communication Technology for Sustainable Development(pp. 277-284). Springer.
Gao, Z., Li, Z., & Niu, K. (2016). Solutions for problems of existing E-commerce recommendation system. 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW),
García, F. J., & Gil, A. B. Personalización de Sistemas de Recomendación. Universidad de Salamanca.
Gatchalee, P., Li, Z., & Supnithi, T. (2013). Ontology development for SMEs E-commerce website based on content analysis and its recommendation system. 2013 International Computer Science and Engineering Conference (ICSEC),
Gkika, S., & Lekakos, G. (2014, 6-7 Nov. 2014). Investigating the Effectiveness of Persuasion Strategies on Recommender Systems. 2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization,
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM,35(12), 61-70.
Hammond, K. J. (2012). Case-based planning: Viewing planning as a memory task. Elsevier.
Herrera, F. (2000). A 2-tuple fuzzy linguistic representation model for computing with words.8(6), 746–752. https://doi.org/10.1109/91.890332
Hu, R., & Pu, P. (2010). A study on user perception of personality-based recommender systems. International Conference on User Modeling, Adaptation, and Personalization,
Huseynov, F., Huseynov, S. Y., & Özkan, S. (2016). The influence of knowledge-based e-commerce product recommender agents on online consumer decision-making. Information Development,32(1), 81-90.
Hwangbo, H., Kim, Y. S., & Cha, K. J. (2018). Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications,28, 94-101.
Itmazi, J. (2005). Sistema flexible de Gestión del eLearning para soportar el aprendizaje en las Universidades tradicionales y abiertas.
Jannach, D., & Friedrich, G. (2013). Tutorial: recommender systems. International Joint Conference on Artificial Intelligence Beijing,
Jiang, L., Cheng, Y., Yang, L., Li, J., Yan, H., & Wang, X. (2019). A trust-based collaborative filtering algorithmfor E-commerce recommendation system. Journal of Ambient Intelligence and Humanized Computing,10(8), 3023-3034.
Ku, Y.-C., Peng, C.-H., &Yang, Y.-C. (2014). Consumer preferences for the interface of e-commerce product recommendation system. International Conference on HCI in Business,
Lorenzi, F., & Ricci, F. (2005). Case-based recommender systems. In Encyclopedia of Data Warehousing andMining(pp. 124-128). IGI Global.
Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems,74, 12-32.
Lü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K., & Zhou, T.(2012). Recommender systems. Physics reports,519(1), 1-49.
Lugo , J. A. (2012). Modelo para el control de la ejecución de proyectos basado en indicadores y lógica borrosa.
Luo, Y. (2012). An analysis of Web mining-based recommender systems for e-commerce. Proceedings of the 2012 International Conference on Computer Application and System Modeling,
Mar Cornelio, O., Bron Fonseca, B., & Gulín González, J. (2020). Sistema de Laboratorios Remoto para el estudio de la Microbiología y Parasitología Médica. Revista Cubana de Informática Médica,12(2).
Maridueña-Arroyave, M. R., & Febles-Estrada, A. (2016). A college degree recommendation model. DYNA,83, 29-34. http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532016000500003&nrm=iso
Martín-Vicente, M. I., Gil-Solla, A., Ramos-Cabrer, M., Blanco-Fernández, Y., & López-Nores, M. (2009). Avoiding fake neighborhoods in e-commerce collaborative recommender systems: A semantic approach. 2009 Fourth International Workshop on Semantic Media Adaptation and Personalization,
Miao, J. F. (2014). Design and implementation of personalization recommendation system in mobile e-commerce. Advanced Materials Research,
Narayanan, T., & McGuinness, D. L. (2008, 10-15 Feb. 2008). Towards Leveraging Inference Web to Support Intuitive Explanations in Recommender Systems for Automated Career Counseling. First International Conference on Advances in Computer-Human Interaction,
Padilla, R. C., Ruiz, J. G., Alava, M. V., & Vázquez, M. L. (2018). Modelo de recomendación basado en conocimiento empleando números SVN. Infinite Study.
Palopoli, L., Rosaci, D., & Sarnè, G. M. (2013). Introducing specialization in e-commerce recommender systems. Concurrent Engineering,21(3), 187-196.
Pathak, B., Garfinkel, R., Gopal, R., Venkatesan, R., & Yin, F. (2010). Empirical Analysis of the Impact of Recommender Systems on Sales. Management Information Systems,27, 159-188.
Pazzani, M. J., Muramatsu, J., & Billsus, D. (1996). Syskill & Webert: Identifying interesting web sites. AAAI/IAAI, Vol. 1,
Penny, S. G., Akella, S., Buehner, M., Chevallier, M., Counillon, F., Draper, C., Frolov, S., Fujii, Y., Karspeck, A., & Kumar, A. (2017). Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations. (201801), 45.
Pu, P., Chen, L., & Kumar, P. (2008). Evaluating product search and recommender systems for E-commerce environments. Electronic Commerce Research,8(1-2), 1-27.
Qiu, Z., Chen, M., &Huang, J. (2010). Design of multi-mode e-commerce recommendation system. 2010 Third International Symposium on Intelligent Information Technology and Security Informatics,
Ramírez, C. (2018). Algoritmo SVD aplicado a los sistemas de recomendación en el comercio. Tecnología, Investigación y Academia (TIA),6 (1), 18-27.
Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM,4(3), 56 -59. https://doi.org/ACM 0002-0782/97/0300
Saini, S., Saumya, S., & Singh, J. P. (2017). Sequential purchase recommendation system for e-commerce sites. IFIP International Conference on Computer Information Systems and Industrial Management,
Smits, G., Nerzic, P., Lesot, M., & Pivert, O. (2019, 23-26 June 2019). FRELS: Fast and Reliable Estimated Linguistic Summaries. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE),
Smits, G., Yager, R. R., & Pivert, O. (2017, 9-12 July 2017). Interactive data exploration on top of linguistic summaries. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE),
Souali, K., El Afia, A., & Faizi, R. (2011). An automatic ethical-based recommender system for e-commerce. 2011 International Conference on Multimedia Computing and Systems,
Tintarev, N., & Masthoff, J. (2007). A survey of explanations in recommender systems. 2007 IEEE 23rd international conference on data engineering workshop,
Vázquez Y., Machín A., Velasteguí E., & V., P. (2018). Genetic algorithm for the generation of prediction rules in moodle courses. Revista electrónica Ciencia Digital,2(1), 7-17. https://doi.org/https://doi.org/10.33262/cienciadigital.v2i1.
Xiaosen, W. (2015). The design and realization of personalized e-commerce recommendation system. 2015 International Conference on Social Science and Technology Education,
Xue, W., Xiao, B., & Mu, L. (2015). Intelligent mining on purchase information and recommendation system for e-commerce. 2015 IEEE international conference on industrial engineering and engineering management (IEEM),
Ya, L. (2010). E-commercepersonalized recommendation technology based on expert system. 6th International Conference on Digital Content, Multimedia Technology and its Applications,
Yang, F., Liu, N., Wang, S., & Hu, X. (2018, 17-20 Nov. 2018). Towards Interpretation of Recommender Systems with Sorted Explanation Paths. 2018 IEEE International Conference on Data Mining (ICDM),
Zhang, L., Luo, T., Zhanga, F., & Wu, Y. (2018). A Recommendation Model Based on Deep Neural Network. IEEE Access(99), 1-10. https://doi.org/10.1109/ACCESS.2018.2789866
