Thursday, August 19, 2004
In this talk, I will describe Community-Driven Adaptation (CDA), a novel technique for automatic content adaptation that adapts content based on feedback provided by users. CDA groups users into communities based on common characteristics (e.g., device type or preferences), and assumes that members of the same community share requirements for adaptation, so that adapted content that is suitable for some members of the community is likely to be acceptable to other members as well. When responding to a mobile user request, CDA first makes an initial prediction on how to adapt the content. Next, CDA monitors the user as she modifies the adapted content to make it more appropriate to her tasks (e.g., make content more readable by removing an irrelevant toolbar, convert text to speech, or increase or reduce a video’s frame rate). The user modifications constitute an implicit feedback mechanism that CDA uses to improve its adaptation predictions for future accesses by other members of the user’s community. Experiments conducted with a prototype that adapts image-rich web pages for browsing over bandwidth-limited links show that compared to existing approaches to automatic adaptation, CDA reduces wastage of network bandwidth by up to 90% and requires less user interaction to correct bad adaptation decisions.
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