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Authors

Scott Bateman

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Scott Bateman

Personal Homepage:
http://www.cs.usask.ca/~ssb609
Current place of employment:
University of Saskatchewan

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Publications by Scott Bateman (bibliography)

» 2009 «

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Bateman, Scott, Muller, Michael J. and Freyne, Jill (2009): Personalized retrieval in social bookmarking. In: GROUP09 - International Conference on Supporting Group Work 2009. pp. 91-94. Available online

“Users of social bookmarking systems take advantage of pivot browsing, an interaction technique allowing them to easily refine lists of bookmarks through the selection of filter terms. However, social bookmarking systems use one-size-fits-all ranking metrics to order refined lists. These generic rankings ignore past user interactions that may be useful in determining the relevance of bookmarks. In this work we describe a personalized ordering algorithm that leverages the fact that refinding, rather than discovery (finding a bookmark for the first time), makes up the majority of bookmark accesses. The algorithm examines user-access histories and promotes bookmarks that a user has previously visited. We investigate the potential of our algorithm using interaction logs from an enterprise social bookmarking system, the results show that our personalized algorithm would lead to improved bookmark rankings.”

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» 2008 «

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Bateman, Scott, Gutwin, Carl and Nacenta, Miguel A. (2008): Seeing things in the clouds: the effect of visual features on tag cloud selections. In: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia 2008. pp. 193-202. Available online

“Tag clouds are a popular method for visualizing and linking socially-organized information on websites. Tag clouds represent variables of interest (such as popularity) in the visual appearance of the keywords themselves -- using text properties such as font size, weight, or colour. Although tag clouds are becoming common, there is still little information about which visual features of tags draw the attention of viewers. As tag clouds attempt to represent a wider range of variables with a wider range of visual properties, it becomes difficult to predict what will appear visually important to a viewer. To investigate this issue, we carried out an exploratory study that asked users to select tags from clouds that manipulated nine visual properties. Our results show that font size and font weight have stronger effects than intensity, number of characters, or tag area; but when several visual properties are manipulated at once, there is no one property that stands out above the others. This study adds to the understanding of how visual properties of text capture the attention of users, indicates general guidelines for designers of tag clouds, and provides a study paradigm and starting points for future studies. In addition, our findings may be applied more generally to the visual presentation of textual hyperlinks as a way to provide more information to web navigators.”

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