000096219 001__ 96219
000096219 005__ 20210902121840.0
000096219 0247_ $$2doi$$a10.1109/ACCESS.2020.3024649
000096219 0248_ $$2sideral$$a120532
000096219 037__ $$aART-2020-120532
000096219 041__ $$aeng
000096219 100__ $$0(orcid)0000-0001-5549-7649$$aFabra, J.$$uUniversidad de Zaragoza
000096219 245__ $$aLog-Based Session Profiling and Online Behavioral Prediction in E-Commerce Websites
000096219 260__ $$c2020
000096219 5060_ $$aAccess copy available to the general public$$fUnrestricted
000096219 5203_ $$aImprovements 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.
000096219 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T21-17R-DISCO$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2017-84796-C2-2-R
000096219 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000096219 590__ $$a3.367$$b2020
000096219 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b65 / 162 = 0.401$$c2020$$dQ2$$eT2
000096219 591__ $$aTELECOMMUNICATIONS$$b36 / 91 = 0.396$$c2020$$dQ2$$eT2
000096219 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b94 / 273 = 0.344$$c2020$$dQ2$$eT2
000096219 592__ $$a0.586$$b2020
000096219 593__ $$aComputer Science (miscellaneous)$$c2020$$dQ1
000096219 593__ $$aMaterials Science (miscellaneous)$$c2020$$dQ1
000096219 593__ $$aEngineering (miscellaneous)$$c2020$$dQ1
000096219 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000096219 700__ $$0(orcid)0000-0002-6584-7259$$aAlvarez, P.$$uUniversidad de Zaragoza
000096219 700__ $$0(orcid)0000-0002-9622-8186$$aEzpeleta, J.$$uUniversidad de Zaragoza
000096219 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000096219 773__ $$g8 (2020), 171834-171850$$pIEEE Access$$tIEEE Access$$x2169-3536
000096219 8564_ $$s1610445$$uhttps://zaguan.unizar.es/record/96219/files/texto_completo.pdf$$yVersión publicada
000096219 8564_ $$s559000$$uhttps://zaguan.unizar.es/record/96219/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000096219 909CO $$ooai:zaguan.unizar.es:96219$$particulos$$pdriver
000096219 951__ $$a2021-09-02-10:22:56
000096219 980__ $$aARTICLE