Computerised Recommendations On E-Transaction Finalisation By Means Of Machine Learning

• Authors: Budnikas Germanas

Abstracts

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; Naïve 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.

Keywords

EN

online behaviour; Google Analytics; Naïve Bayes classifier; artificial neural network

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2015
• Volume: 16
• Issue: 2
• Pages: 309-322

Contributors

author

Budnikas Germanas

• University of Bialystok, Faculty of Economics and Informatics in Vilnius. Kaunas University of Technology, Faculty of Informatics, Lithuania

References

• ANGELETOU, S., ROWE, M., ALANI, H., (2011). Modelling and Analysis of User Behaviour in Online Communities. The Semantic Web – ISWC 2011 (pp. 35−50). Lecture Notes in Computer Science Volume 7031.
• BUDNIKAS, G., (2015). Creation of user online behaviour analysis model for increase of an enterprise competitiveness. Rzeszów: In proceedings of VI Ogólnopolska Konferencja Naukowa „Społeczeństwo Informacyjne. Stan i kierunki rozwoju w świetle uwarunkowań regionalnych" (in press).
• CLIFTON, B., (2012). Advanced Web Metrics with Google Analytics (3rd Edition ed.). Indianapolis: John Wiley & Sons.
• DEMBCZYŃSKI, K., KOTŁOWSKI, W., SYDOW, M., (2009). Effective Prediction of Web User Behaviour with User-Level Models. Journal Fundamenta Informaticae, 89(2−3), 189−206.
• DREJEWICZ, S., (2012). Zrozumieć BPMN. Modelowanie procesów biznesowych. Helion.
• MULPURU, S., HULT, P., MCGOWAN, B., (2010, May 20). Understanding Shopping Cart Abandonment. Retrieved June 25, 2015, from https://www.forrester.com/Understanding+Shopping+Cart+Abandonment/fulltext/-/E-RES56827
• NIKIFORAKIS, N., ACAR, G., SAELINGER, D., (2014). Browse at your own risk. Spectrum, IEEE, 51(8), 30−35.
• ROBINSON, D. J. B. V., (2008). Online Behavioural Analysis and Modeling Methodology (OBAMM). Social Computing, Behavioural Modeling, and Prediction, 100−109.
• RUSSELL, S. A., (2010). Artificial Intelligence: International Version: A Modern Approach (3 ed.). Pearson.
• WHITE, R. W., CHU, W., HASSAN, A., HE, X., SONG, Y., WANG, H., (2013). Enhancing personalized search by mining and modeling task behavior. Proceedings of the 22nd International Conference on World Wide Web (pp. 1411–1420). ACM.
• XIAN, X., CHEN, F., WANG, J., (2014). An Insight into Campus Network User Behavior Analysis Decision System. (pp. 537−540). Taichung: IEEE.