The large-scale patterns of accretion and erosion that are produced by end-user interactions can be harnessed to significant advantage. The paths that individuals trace through their use of digital media and interfaces provide opportunities for both empirical insights and support for novel systems. For this talk, I'm going to relate some of our experiences in mining these traces to support applications ranging from social feed ranking to pushing Photoshop into the cloud. I'll talk about a few specific systems--Butterworth, TappCloud, NewsVis--which all demonstrate this idea, but will also try to describe the approach that has made these successful. In particular, I'll focus on some of the unconventional, non-reactive, sources of data we have been able to leverage and how they might be transformed. By looking to large-scale networks to solve text problems, or looking to Web-scale text collections instead of the single end-user's behavior, we're able to improve the user experience for many different problems in dramatic ways.
Eytan Adar is an Assistant Professor in the School of Information & Computer Science and Engineering at the University of Michigan. He completed his doctoral work in the Computer Science and Engineering Department at the University of Washington. He works in the area of temporal-informatics, studying how large populations interact with each other and with the dynamic Web and how those interactions can be enhanced. His interests are in understanding the dynamics of user behavior and data on the Web through text and log analysis, visualization, and the creation of new tools. Before graduate school, Eytan was a researcher at HP Labs and Xerox PARC for a number of years (spinning out a company called Outride somewhere in there). He received his Master of Engineering and Bachelor of Science degrees from the Massachusetts Institute of Technology. Eytan was co-founder of ICWSM and is co-general chair of the conference this year.
Faculty Host: Laura Dabbish
jpuglisi [atsymbol] cs.cmu.edu