School of Information and Library Science (SILS) graduate student, Felix Portnoy, is the recipient of a $50,000 award from Google's Research Award program for his proposal, "Modeling the Effect of Habituation on Banner Blindness as a Function of Repetition and Search Type." The award will support Portnoy's current research on banner blindness.
Each submission for a research award is thoroughly reviewed by teams of Google researchers and engineers.
"I am very grateful and excited to have the opportunity to pursue my research interests at SILS," said Portnoy. "Thanks to Google's generous contribution, we will be able to further expand the boundaries of scientific knowledge to reveal the underlying behavioral model of user interaction with online media."
Banners are used on Web sites to advertise or highlight specific announcements. According to the research, in "1994, the click-through rate (CTR) of online banners has been continuously declining from an astonishing 78 percent to the present industry average of 1 percent to 2 percent. This drastic decline led some publishers to conclude that the prevalent model of free content paid by ads is unsustainable. Previous studies found that the cause for the CTR decline lies in repeated exposure to the banners that leads to banner blindness."
Portnoy and his advisor, Dr. Gary Marchionini, dean of SILS, wrote a paper that explains in more detail one of the possible causes of banner blindness entitled, Modeling the Effect of Habituation on Banner Blindness as a Function of Repetition and Search Type: Gap analysis for future work.
With the additional funds from Google, Portnoy's study will focus on several research goals including:
1. Establish a behavioral model that describes the effect of habituation on the perception of online banners.
2. Investigate the effect of search task type, exploratory and goal-driven, on orienting response to online banners.
3. Develop and test mitigation techniques to inhibit the habituation effect and increase the users’ perception of online banners, while maintaining a positive user experience.
4. Assess the correlations among different biometric (eye movement, heart rate, galvanic skin response) indicators of attention and habituation.