A platform for large-scale machine learning on web design

Authors:
Arvind Satyanarayan;Maxine Lim;Scott Klemmer
Affiliations:
Stanford University, Stanford, California, USA;Stanford University, Stanford, California, USA;Stanford University, Stanford, California, USA
Venue:
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Year:
2012

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Abstract

The Web is an enormous and diverse repository of design examples. Although people often draw from extant designs to create new ones, existing Web design tools do not facilitate example reuse in a way that captures the scale and diversity of the Web. To do so requires using machine learning techniques to train computational models which can be queried during the design process. In this work-in-progress, we present a platform necessary for doing such large-scale machine learning on Web designs, which consists of a Web crawler and proxy server to harvest and store a lossless and immutable snapshot of the Web; a page segmenter that codifies a page's visual layout; and an interface for augmenting the segmentations with crowdsourced metadata.