Data-driven interactions for web design

  • Authors:
  • Ranjitha Kumar

  • Affiliations:
  • Stanford University, Stanford, California, USA

  • Venue:
  • Adjunct proceedings of the 25th annual ACM symposium on User interface software and technology
  • Year:
  • 2012

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Abstract

This thesis describes how data-driven approaches to Web design problems can enable useful interactions for designers. It presents three machine learning applications which enable new interaction mechanisms for Web design: rapid retargeting between page designs, scalable design search, and generative probabilistic model induction to support design interactions cast as probabilistic inference. It also presents a scalable architecture for efficient data-mining on Web designs, which supports these three applications.