Correlation-Based Content Adaptation for Mobile Web Browsing

The resource impoverished environment on mobile devices results in a poor experience for users browsing the World Wide Web. Proxy-based middleware that transform content on the fly to better suit the resource conditions on a user’s device provide a promising solution to this problem. A key challenge in such systems is deciding how to adapt content, especially when the same content has multiple uses that have varying adaptation requirements. In this paper, we show that it is possible to provide fine grain adaptation of multi-purpose content by detecting correlations in the adaptation requirements of past users across multiple objects on a web site, and using this history to make adaptation predictions for users encountered subsequently. To evaluate our technique, we built prototype page layout and image fidelity adaptation systems, and used these to gather traces from users browsing multi-purpose web content in a laboratory setting. Our experimental results show that using correlations to make adaptation predictions can significantly reduce bandwidth consumption, browsing time, energy usage and user effort required to adapt content.