Data-driven interactions for web design

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationAdjunct Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology, UIST'12
Pages51-54
Number of pages4
DOIs
StatePublished - Nov 19 2012
Externally publishedYes
Event25th Annual ACM Symposium on User Interface Software and Technology, UIST 2012 - Cambridge, MA, United States
Duration: Oct 7 2012Oct 10 2012

Publication series

NameAdjunct Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology, UIST'12

Other

Other25th Annual ACM Symposium on User Interface Software and Technology, UIST 2012
Country/TerritoryUnited States
CityCambridge, MA
Period10/7/1210/10/12

Keywords

  • Machine learning
  • Web design

ASJC Scopus subject areas

  • Software

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