Automated Personalisation of Internet Users Using Self-Organising Maps

Authors:
Yrjö Hiltunen;Mika Lappalainen
Affiliations:
-;-
Venue:
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Year:
2002

Citing 3
Cited 1

Recommender systems in e-commerce

Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce

Proceedings of the 2nd ACM conference on Electronic commerce
Self-Organizing Maps

Self-Organizing Maps

Detection of malicious and non-malicious website visitors using unsupervised neural network learning

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automated personalisation offers a major improvement in the use of large Web sites. These systems learn from a user and suggest where on the Web site a user might move. Self-organising maps (SOM) may also be considered as a potential tool for Web data analysis. In this paper, the use of SOM analysis for automated personalisation of Internet users is demonstrated. The map was obtained by training a self-organising network with user demographics; click stream data were used to calculate the probabilities of user behaviour on the Web site. Thus, the map can be used for personalisation of users and to calculate the probabilities of each neuron in predicting where the user will next move on the Web site. The results indicate that SOM analysis can successfully process Web information.