Paper Info
Title
Data-driven interactions for web design
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.
Year
DOI
Venue
2012
10.1145/2380296.2380318
UIST (Adjunct Volume)
Keywords
Field
DocType
scalable architecture,generative probabilistic model induction,web design,efficient data-mining,data-driven approach,data-driven interaction,design interaction,probabilistic inference,page design,scalable design search,web design problem,machine learning
Web design,Data-driven,Computer science,Retargeting,Web modeling,Human–computer interaction,Statistical model,Generative grammar,Social Semantic Web,Generative Design
Conference
Citations 
PageRank 
References 
0
0.34
13
Authors
1
Name
Order
Citations
PageRank
Ranjitha Kumar131319.54