Personalised online sales using web usage data mining

✍ Scribed by Xuejun Zhang; John Edwards; Jenny Harding

• Book ID: 104015586
• Publisher: Elsevier Science
• Year: 2007
• Tongue: English
• Weight: 830 KB
• Volume: 58
• Category: Article
• ISSN: 0166-3615
• DOI: 10.1016/j.compind.2007.02.004

✦ Synopsis

Practically every major company with a retail operation has its own web site and online sales facilities. This paper describes a toolset that exploits web usage data mining techniques to identify customer Internet browsing patterns. These patterns are then used to underpin a personalised product recommendation system for online sales. Within the architecture, a Kohonen neural network or self-organizing map (SOM) has been trained for use both offline, to discover user group profiles, and in real-time to examine active user click stream data, make a match to a specific user group, and recommend a unique set of product browsing options appropriate to an individual user. Our work demonstrates that this approach can overcome the scalability problem that is common among these types of system. Our results also show that a personalised recommender system powered by the SOM predictive model is able to produce consistent recommendations.