Enabling scalable online personalization on the web

Abstract

Online personalization is of great interest to e-companies. Virtually all personalization technologies are based on the idea of storing as much historical customer session data as possible, and then querying the data store as customers navigate through a web site. The holy grail of on-line personalization is an environment where fine-grained, detailed historical session data can be queried based on current online navigation patterns to formulate real-time responses. Unfortunately, as more consumers become e-shoppers, the user load and the amount of historical data continue to increase, causing scalability-related problems for almost all current personalization technologies. This paper chronicles the development of a real-time interaction management engine through the integration of historical data and on-line visitation patterns of e-commerce site visitors. This paper describes the scientific underpinnings of the system, as well as the architecture and a performance evaluation The experimental evaluation shows that our caching and storage techniques deliver performance that is orders of magnitude better than those derived from off-the-shelf database components.