Web-based User Profiling using a Recurrent Neural Network

R. MacDonald and D.L. Silver (Canada)


User profiling, adaptive interface, artificial neural networks, e-commerce, click-stream analysis


The Internet based electronic market has grown so rapidly that companies must now strive to not only have a presence on the Internet but to differentiate themselves from their competitors. Personalization of the web shopping experience is one approach that E-Commerce companies can take to attract and retain customers. This paper describes a collaborative user profiling and adaptive interface method embedded within a fictitious E commerce website. The system combines JavaScript, Java Applet and Artificial Neural Network (ANN) technologies to produce web pages with dynamic content that recommend the next best links for a customer based on the interactions of prior customers. Tests with human subjects show (1) the effectiveness of the system when compared to a base-line system that randomly recommends best links and (2) the advantage of the system to users who were not informed of the purpose of the recommendation links.

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