Community-driven adaptation: automatic content adaptation in pervasive environments
Iqbal Mohomed, Alvin Chin, Jim Chengming Cai, Eyal de Lara
Abstract
Iqbal Mohomed, Alvin Chin, Jim Chengming Cai, Eyal de Lara
Abstract
Mobile devices are increasingly being used to access Web content but lack the resources for proper presentation to the user. To address this problem, content is typically adapted to be more suitable for a mobile environment. Community-driven adaptation (CDA) is a novel approach to automatic content adaptation for mobile devices that adapts content based on feedback from users. CDA groups users into communities based on common characteristics, and assumes that users of the same community have similar adaptation requirements. CDA learns how to adapt content by observing how members of a community alter adapted content to make it more useful to them. Experiments that consider the idealized case, where all users perform the same task, show that CDA can reduce wastage of network bandwidth by up to 90% and requires less user interaction to correct bad adaptation decisions compared with existing approaches to automatic content adaptation.
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Mobile devices are increasingly being used to access Web content but lack the resources for proper presentation to the user. To address this problem, content is typically adapted to be more suitable for a mobile environment. Community-driven adaptation (CDA) is a novel approach to automatic content adaptation for mobile devices that adapts content based on feedback from users. CDA groups users into communities based on common characteristics, and assumes that users of the same community have similar adaptation requirements. CDA learns how to adapt content by observing how members of a community alter adapted content to make it more useful to them. Experiments that consider the idealized case, where all users perform the same task, show that CDA can reduce wastage of network bandwidth by up to 90% and requires less user interaction to correct bad adaptation decisions compared with existing approaches to automatic content adaptation.
Key concepts: Content adaptation, Adaptation (eye), Computer science, Mobile device, User-generated content, Web content, Human–computer interaction, World Wide Web