User adaptive Web morphing : an implementation of a Web-based Bayesian inference engine with Gittins' Index

Abstract

Imagine a world where computers are able to present desired information to people in the most relevant and effective way possible, where machines are able to adapt the way they interact with humans when they encounter different personality styles. Web Morphing captures the essence of this idea and applies it to realm of Digital Marketing, allowing companies to present product information in a manner in which the consumers are most comfortable with. By using user click-history, a Website with Morphing capability can display its information based on the user's inferred Cognitive and Cultural Styles. This thesis documents the process of building the Mathematical Inference Engine of a Web Morphing System that gives a Web site the ability to adapt itself to individual users. First, I will briefly discuss the history and motivation of Morphing. Then, I will discuss the theory of Morphing from the work of Hauser, Urban, Liberali, and Braun, and I will give a system overview of the Web Morphing System. The main contribution of the thesis is the technical implementation of the Mathematical Inference Engine, and I will describe in detail the construction of Mathematical Inference Engine's two major parts: the Bayesian Inference Engine, and the Gittins' Index Engine.