Log-Based Session Profiling and Online Behavioral Prediction in E-Commerce Websites

Fabra, J. (Universidad de Zaragoza) ; Alvarez, P. (Universidad de Zaragoza) ; Ezpeleta, J. (Universidad de Zaragoza)

Resumen: Improvements to customer experience give companies a competitive advantage, as understanding customers' behaviors allows e-commerce companies to enhance their marketing strategies by means of recommendation techniques and the customization of products and services. This is not a simple task, and it becomes more difficult when working with anonymous sessions since no historical information of the user can be applied. In this article, analysis and clustering of the clickstreams of past anonymous sessions are used to synthesize a prediction model based on a neural network. The model allows for prediction of a user's profile after a few clicks of an online anonymous session. This information can be used by the e-commerce's decision system to generate online recommendations and better adapt the offered services to the customer's profile.

Idioma: Inglés

DOI: 10.1109/ACCESS.2020.3024649

Año: 2020

Publicado en: IEEE Access 8 (2020), 171834-171850
ISSN: 2169-3536

Factor impacto JCR: 3.367 (2020)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 65 / 162 = 0.401 (2020) - Q2 - T2
Categ. JCR: TELECOMMUNICATIONS rank: 36 / 91 = 0.396 (2020) - Q2 - T2
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 94 / 273 = 0.344 (2020) - Q2 - T2

Factor impacto SCIMAGO: 0.586 - Computer Science (miscellaneous) (Q1) - Materials Science (miscellaneous) (Q1) - Engineering (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T21-17R-DISCO
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2017-84796-C2-2-R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)