Propensity Modelling for Intelligent Content

Abstract

Intelligent Content was proposed by Ann Rockley as content that is structurally rich and semantically aware and therefore automatically discoverable, reusable, reconfigurable, and adaptable. Currently, the majority of approaches to achieving Intelligent Content are service-side, where cloud services discover and transform content before delivering it to a client or user. An alternative approach to achieving Intelligent Content, embeds the intelligence within the content itself, imbuing the content with the ability to call services and enact discovery, reusability, reconfiguration and adaption on the client-side.Thus, the Intelligent Content can be proactive in calling cloud services and perform contextually relevant transformations or behaviours. This work explores this client-side approach to Intelligent Content and aims to examine the content-service interactions in such a system. The research follows a case-study based approach, examining the development of a client-side user model utilised to personalise and cache content on the client-side. Specifically, the research examines a propensity model, monitoring implicit user actions such as mouse movement and scrolling to create content that knows the propensity of a user to click on various page elements. Evaluation of the proposed architecture will examine the impact of content-service interaction frequency on system accuracy and response time.