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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Classification Of E-Customer Sessions Based On Support
Vector Machine |
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Authors: |
Grazyna Suchacka,
Magdalena Skolimowska-Kulig, Aneta
Potempa |
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Published in:
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(2015).ECMS 2015 Proceedings edited
by: Valeri M. Mladenov, Grisha Spasov, Petia Georgieva, Galidiya Petrova, European
Council for Modeling and Simulation. doi:10.7148/2015 ISBN:
978-0-9932440-0-1 29th
European Conference on Modelling and Simulation, Albena (Varna), Bulgaria,
May 26th – 29th,
2015 |
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Citation
format: |
Grazyna Suchacka,
Magdalena Skolimowska-Kulig, Aneta
Potempa (2015). Classification Of E-Customer
Sessions Based On Support Vector Machine, ECMS 2015 Proceedings edited by: Valeri M. Mladenov, Petia Georgieva, Grisha Spasov, Galidiya Petrova European
Council for Modeling and Simulation. doi:10.7148/2015-0594 |
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DOI: |
http://dx.doi.org/10.7148/2015-0594 |
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Abstract: |
A key feature of high-traffic
e-commerce sites is the ability to offer a predictive and personalized
service to Web users. Visitors to online stores are potential buyers but in
reality very few visits finally result in a product purchase. Thus, it would
be especially valuable for online retailers to predict buyers against
browsers based on some session features (e.g. session duration, the number of
downloaded pages, the kind of realized Web interactions) and some HTTP-level
information (the number of HTTP requests, the volume of data transfer in
session). In this paper, we recast online purchase pre- dictions as a
classification problem. Every user session in a web store is represented as a
23-element vector in the session feature space. Based on historical data from
an online bookstore an SVM classification model is proposed, dividing user
sessions into two classes: browsing sessions and buying sessions. The best
SVM classifier proved to be very effective, with a predictive accuracy of
over 99% and the probability of predicting a buying session of almost 95%. |
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