Data-driven interactions for web design

Ranjitha Kumar
2012 Adjunct proceedings of the 25th annual ACM symposium on User interface software and technology - UIST Adjunct Proceedings '12  
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.
doi:10.1145/2380296.2380318 dblp:conf/uist/Kumar12 fatcat:2bt4pyqsu5h3be3ayhsjjofkrm