Abstract
A recommended system (RS) seeks to predict the preference that a user would give to a product in use, provides personalized information for the identification of articles, generating suggestions that are beneficial and agile for the search of the required items or activities. The user can accept the recommendations by providing information that is stored in a database, and generates new suggestions. These systems are used in the most prominent platforms such as websites and social networks. These information filtering techniques focus on the main properties and characteristics of items and users. This paper presents an analysis of the recommended systems and the components involved in the development of their functions. It shows an individual approach to filtering techniques, classification of RSs, possible combinations of filtering techniques and finally the conclusions are obtained in the analysis of the Recommended Systems.
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References
Joyanes L (2014) Big data: análisis de grandes volúmenes de datos en organizaciones. Barcelona: Marcombo. Ediciones Técnicas. La gestión de la identidad digital: Una nueva habilidad informacional y digital. BID. Universidad de Barcelona https://bid.ub.edu/24/giones2.htm 2010
Silva J, Cubillos J, Villa JV, Romero L, Solano D (2019) Preservation of confidential information privacy and association rule hiding for data mining: a bibliometric review. Proc Comput Sci 151:1219–1224. R
Solis Jacqueline, Chacón-Rivas Mario Garita Cesar (2014) Agente Híbrido Recomendador de Objetos de Aprendizaje. Obtenido de Research Gate: https://www.researchgate.net/publication/270341748_Agente_Hibrido_ Recomendador_de_Objetos_de_Aprendizaje
Tovar L, Montoya J, Martelo R (2018) Sistema ecléctico de filtrado de información basado en inteligencia computacional para recomendación de atractivos turísticos del Caribe Colombiano. Recuperado el 11 de 07 de 2018, de https://revistas.sena. edu.co/index.php/LOG/article/download/1521/1692
Viloria A, Viviana Robayo P (2016) Virtual network level of application composed IP networks connected with systems - (NETS Peer-to- Peer). Indian J Sci Technol 9(46). doi:https://doi.org/10.17485/ijst/2016/v9i46/107376
Gonzaga-Perez L (2009) Modelos de recomendación con falta de información. Retrieved in 11 de 07 de 2018, de Catálogo del Centro de Documentación y Publicaciones del Servicio de Información y Documentación, Estudios y Publicaciones de la Secretaría General Técnica de la Consejería de Turismo, Comercio y Deporte de la Junta de Andalucía: https://www.juntadeandalucia.es/turismocomercioydeporte/publicaciones/36570.pdf
Isinkaye F, Folajimi Y, Ojokoh B (2015) Recommendation systems: principles, methods and evaluation. Egyptian Inf J, pp 261–273. Retrieved from: https://doi.org/https://doi.org/10.1016/J.EIJ.2015.06.005
Moreno A, Redondo T (2016) Text analytics: the convergence of big data and artificial intelligence. En international. J Int Mult Art Intel
Silva J, Solano D, Fernandez C, Romero L, Villa JV (2019) Privacy preserving, protection of personal data, and big data: a review of the Colombia case. Proc Comput Sci 151:1213–1218
Benitez R, Escudero G, Kanaan SM (2013) Inteligencia artificial avanzada. Barcelona: UOC
Bennet J, Lanning S (2007) The Netflix prize. Proceedings of KDD Cup and Worksho, pp 3–6
Linyuan L, Matúš Medo, Chi Ho Y, Yi-Cheng Z, Zi-Ke Z, Tao Z (2012) Recommender systems. Elsevier B.V, pp 1–49
López Puga JG (2007) Las redes bayesianas como herramientas de modelado en psicología. Anales de Psicología. Redalyc, pp 307–3016
Matich DJ (2001) Redes neuronales: conceptos básicos y aplicaciones. Cátedra de Informática Aplicada a la Ingeniería de Procesos-Orientación, Rosario
Lee J, Lee D, Lee YC, Hwang WS, Kim SW (2016) Improving the accuracy of top-n recommendation using a preference model. Inf Sci 348(1):290–304
Silva J, Varela N, Lezama OBP, Hernández-P H, Ventura JM, de la Hoz B, Coronel LP (2019, July) Multi-dimension tensor factorization collaborative filtering recommendation for academic profiles. In International Symposium on Neural Networks (pp. 200–209). Springer, Cham
Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 4(1):1–12
Malik F, Baharudin B (2013) Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain. J King Saud Univ Comput Inf Sci 25(2):207–218
Isinkaye FO, Folajimi YO, Ojokoh BA (2015) Recommendation systems: principles, methods and evaluation. Egypt Inform J 16(3):261–273
Taneja A, Arora A (2018) Cross domain recommendation using multidimensional tensor factorization. Expert Syst Appl 92(1):304–316
Panniello U, Tuzhilin A, Gorgoglione M (2014) Comparing context-aware recommender systems in terms of accuracy and diversity. User Model User-Adap Inter 24(2):35–65
Zheng C, Haihong E, Song M, Song J (2016) CMPTF: contextual modeling probabilistic tensor factorization for recommender systems. Neurocomputing 205(1):141–151
Lops P, de Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook, pp 73–105. Springer, Boston (2011). https://doi.org/https://doi.org/10.1007/978-0-387-85820-3_3
Gómez S, Zervas P, Sampson DG, Fabregat R (2014) Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. J King Saud Univ Comput Inf Sci 26(1):47–61
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Neira, H., Guliany, J.G., Vásquez, L.C. (2021). Multidimension Tensor Factorization Collaborative Filtering Recommendation. In: Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Proceedings of International Conference on Big Data, Machine Learning and Applications. Lecture Notes in Networks and Systems, vol 180. Springer, Singapore. https://doi.org/10.1007/978-981-33-4788-5_14
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