An architecture to support scalable online personalization on the Web

Online personalization is of great interest to e-companies. Virtually all p ersonalization technologies are based on the idea of storing as much histor ical customer session data as possible. and then querying the data store as customers navigate through a web site. The holy grail of online personaliz ation is an environment where fine-grained, detailed historical session dat a can be queried based on current online navigation patterns for use in for mulating real-time responses. Unfortunately, as more consumers become e-sho ppers. the user load and the amount of historical data continue to increase . causing scalability-related problems for almost all current personalizati on technologies. This paper chronicles the development of a real-time inter action management system through the integration of historical data and onl ine visitation patterns of e-commerce site visitors. It describes the scien tific underpinnings of the system as well as its architecture. Experimental evaluation of the system shows that the caching and storage techniques bui lt into the system deliver performance that is orders of magnitude better t han those derived from off-the-shelf database components.