- Autor
- Budnikas Germanas (University of Bialystok)
- Tytuł
- Computerised Recommendations on E-Transaction Finalisation by Means of Machine Learning
- Źródło
- Statistics in Transition, 2015, vol. 16, nr 2, s. 309-322, rys., tab., biblogr. s. 321-322
- Słowa kluczowe
- Zachowania informacyjne, Użytkownicy internetu, Sztuczne sieci neuronowe (SSN), Transakcje wirtualne, Uczenie maszynowe
Information behaviours, Internet users, Artificial neural networks (ANN), Virtual transaction, Machine learning - Uwagi
- summ.
Materiały z konferencji Multivariate Statistical Analysis 2014, Łódź. - Abstrakt
- 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)
- Dostępne w
- Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu - Pełny tekst
- Pokaż
- Bibliografia
- 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.
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- 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.
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- Cytowane przez
- ISSN
- 1234-7655
- Język
- eng