Computerised Recommendations on E-Transaction Finalisation by Means of Machine Learning

• Autorzy: Germanas Budnikas
• Języki publikacji: EN

Abstrakty

EN

Nowadays a vast majority of businesses are supported or executed online. Website-to-user interaction is extremely important and user browsing activity on a website is becoming important to analyse. This paper is devoted to the research on user online behaviour and making computerised advices. Several problems and their solutions are discussed: to know user behaviour online pattern with respect to business objectives and estimate a possible highest impact on user online activity. The approach suggested in the paper uses the following techniques: Business Process Modelling for formalisation of user online activity; Google Analytics tracking code function for gathering statistical data about user online activities; Nawe Bayes classifier and a feedforward neural network for a classification of online patterns of user behaviour as well as for an estimation of a website component that has the highest impact on a fulfilment of business objective by a user and which will be advised to be looked at. The technique is illustrated by an example. (original abstract)

Słowa kluczowe

EN

Information behaviours; Internet users; Artificial neural networks (ANN); Virtual transaction; Machine learning

PL

Zachowania informacyjne; Użytkownicy internetu; Sztuczne sieci neuronowe (SSN); Transakcje wirtualne; Uczenie maszynowe

• Czasopismo: Statistics in Transition
• Rocznik: 2015
• Tom: 16
• Numer: nr 2
• Strony: 309--322

Twórcy

autor

Germanas Budnikas

• University of Bialystok

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