A virtual wallet product recommender system based on collaborative filtering

Publicación:
A virtual wallet product recommender system based on collaborative filtering

dc.contributor.advisor	Galpin, Ixent
dc.contributor.advisor	Granados, Oscar
dc.contributor.advisor	Galpin, I.
dc.contributor.advisor	Galpin, Ixent
dc.coverage.spatial	Colombia	spa
dc.creator.degree	Magíster en Ingeniería y Analítica de Datos	spa
dc.date.accessioned	2020-09-15T16:20:45Z
dc.date.available	2020-09-15T16:20:45Z
dc.description.abstract	Hoy en día existen varias opciones a la hora de hacer uso de productos que faciliten los servicios financieros a las personas a través de billeteras virtuales. Un sistema de recomendación proporciona rápidamente a los clientes lo que buscan y les ayuda a descubrir nuevos productos que les gustan. En este trabajo se propone un sistema de recomendación que se puede personalizar de acuerdo a las variables implementadas por Movii, empresa del sector FinTech colombiano, tomando como insumo los registros de transacciones que indican la frecuencia de uso de cada producto, que pueden entenderse como calificaciones. de estos productos. Para determinar el modelo que implementará el sistema de recomendación que se desplegará, se evalúan diferentes modelos, como las técnicas basadas en el filtrado colaborativo. En nuestra evaluación, encontramos que el modelo que recomienda los productos más populares es el que ofrece el mejor rendimiento al recomendar un producto a los usuarios. Así, es posible generar algunas recomendaciones estimadas sobre los servicios disponibles por la empresa, involucrando a los usuarios que consumen los servicios disponibles.	spa
dc.description.abstractenglish	Nowadays, there are several options when it comes to making use of products that facilitate financial services to people through virtual wallets. A recommender system quickly provides customers with what they are looking for and helps discover new products that they like. In this paper, a recommender system is proposed that can be customized according to the variables implemented by Movii, a company in the Colombian FinTech sector, taking as input transaction records that indicate the frequency of use of each product, which can be understood as ratings of these products. To determine the model that will implement the recommender system that will be deployed, different models are evaluated, such as techniques based on collaborative filtering. In our evaluation, we found that the model that recommends the most popular products is the one that offers the best performance in recommending a product to users. Thus, it is possible to generate some estimated recommendations on the services available by the company, involving users who consume the available services.	spa
dc.format.extent	12 páginas	spa
dc.format.mimetype	image/jepg	spa
dc.identifier.repourl	http://expeditio.utadeo.edu.co	spa
dc.identifier.uri	https://hdl.handle.net/20.500.12010/13271
dc.language.iso	spa	spa
dc.publisher	Universidad de Bogotá Jorge Tadeo Lozano	spa
dc.publisher.program	Maestría en Ingeniería y Analítica de Datos	spa
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dc.rights.accessrights	info:eu-repo/semantics/restrictedAccess	spa
dc.rights.local	Acceso restringido	spa
dc.source	instname:Universidad de Bogotá Jorge Tadeo Lozano	spa
dc.source	reponame:Expeditio Repositorio Institucional UJTL	spa
dc.subject	Sistema de recomendación	spa
dc.subject	Fintech	spa
dc.subject	Filtrado colaborativo	spa
dc.subject	Predicción	spa
dc.subject.keyword	Recommender system	spa
dc.subject.keyword	Fintech	spa
dc.subject.keyword	Collaborative filtering,	spa
dc.subject.keyword	Prediction	spa
dc.subject.lemb	Aplicaciones móviles	spa
dc.subject.lemb	Aplicaciones Web	spa
dc.subject.lemb	Software de aplicación	spa
dc.title	A virtual wallet product recommender system based on collaborative filtering	spa
dc.type.coar	http://purl.org/coar/resource_type/c_2df8fbb1	spa
dc.type.driver	info:eu-repo/semantics/masterThesis	spa
dc.type.hasversion	info:eu-repo/semantics/acceptedVersion	spa
dc.type.local	Trabajo de grado de maestría	spa
dspace.entity.type	Publication
relation.isAdvisorOfPublication	62b7a41a-cabb-4bd9-8ac6-c7fa279941ec
relation.isAdvisorOfPublication.latestForDiscovery	62b7a41a-cabb-4bd9-8ac6-c7fa279941ec