Community-Driven Adaptation: Automatic Content Adaptation in Pervasive Environments
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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.Keywords:
Content Adaptation
User-Generated Content
Web content
Mobile content adaptation is a technology of effectively representing the contents originally built for the desktop PC on wireless mobile devices. Previous approaches for Web content adaptation are mostly device-dependent. Also, the content transformation to suit to a smaller device is done manually. As a result, the user has difficulty in selecting relevant information from a heavy volume of contents since the context information related to the content is not provided. To resolve these problems, this paper proposes an enhanced method of Web content adaptation for mobile devices. In our system, the process of Web content adaptation consists of 4 stages including block filtering, block title extraction, block content summarization, and personalization through learning. As a result of learning, personalization is realized by showing the information for the relevant block at the top of the content list
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