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Crowdsourcing Interface for Collecting Correspondences of Web Pages
Abstract: One challenge in building a web design tool that attempts to leverage examples is gathering design alternatives and providing mappings between web page elements. We present a crowdsourcing interface to collect user-generated correspondences between two web pages. Our iterative refinement of the interface was guided by three main design principles: modularize the task, minimize user errors, and provide relevant information. As an initial experiment, we collected fifteen web pages with diverse style and layout, and deployed the interface on Amazon's Mechanical Turk. Preliminary data analysis shows that Turkers take longer than experts and define fewer mappings in general. Further evaluation and experiments with different types of pages will identify directions for a web design tool that enables the use of any web page as a design template.
Papers: , Ranjitha Kumar, and Scott R Klemmer. Crowdsourcing Interface for Collecting Correspondences of Web Pages. UIST '09 Poster, Victoria, BC, Canada 2009. (PDF)
Video:
Retargeting Web Page Content
Abstract: We present a novel technique for automatically retargeting content from one web page onto the layout of another. Web pages are decomposed into their perceptual hierarchical representations. We then use a structured-prediction algorithm to learn reasonable mappings between the perceptual trees. Using the mappings, we are able to merge the content of one page with the layout of another.
Papers: Ranjitha Kumar, , and Scott R Klemmer. Automatic Retargeting of Web Page Content. CHI '09 extended abstracts on Human factors in computing systems, Boston, USA 2009. (PDF)
Interactive Evolution of Topic Maps
Abstract: This paper proposes interactive evolution of topic maps that can suggest new and creative knowledge. Interactive genetic algorithm is applied into topic maps, accepting human evaluation on feasibility of intermediate topic maps. In the proposed system, automated neural network raters are used in order to prevent user fatigue problem. Unlike traditional domains where convergence is valued, this system focuses on maintaining diversity in population, since creativity rather than optimization is needed. Experimental results show that automatic raters perform worse than humans, easily falling into over-fitting problem. Further work can improve the quality of generated topic maps by accepting complex expressions about the search space and preventing over-convergence.
Papers:
, Won-Wook Hong, Robert Ian McKay, and Xuan Hoai Nguyen. Interactive Evolution for Topic Maps with Automated Neural Network Raters.(In Review)
, Won-Wook Hong, and Robert Ian McKay. Evolutionary Topic Maps. Proc. of KHCI 2009. (PDF in Korean)
, Won-Wook Hong, and Robert Ian McKay. Evolutionary Topic Maps. Proc. of KHCI 2009. (PDF in Korean)
Social Network + Metaverse
Interaction Design Methodologies Applicable to Social Network Service Associated with Metaverse.
