In this monthly interview series, we turn the spotlight on members of the academic community and the important research they do — as partners, collaborators, consultants, or independent contributors.
For February, we nominated Bart Knijnenburg, assistant professor at Clemson University. Knijnenburg is a 2019 UX-sponsored research award recipient in improving ad experiences, whose resulting research was nominated for Best Paper at the 54th Hawaii International Conference on System Sciences (HICSS). Knijnenburg has also been involved in the Facebook Fellowship Program as the adviser of two program alumni, Moses Namara and Daricia Wilkinson.
In this Q&A, Knijnenburg describes the work he does at Clemson, including his recently nominated research in improving ad experiences. He also tells us what inspired this research, what the results were, and where people can learn more.
Q: Tell us about your role at Clemson and the type of research you and your department specialize in.
Bart Knijnenburg: I am an assistant professor in the Human-Centered Computing division of the Clemson University School of Computing. Our division studies the human aspects of computing through user-centered design and user experiments, with faculty members who study virtual environments, online communities, adaptive user experiences, etc. My personal interest lies in helping people make better decisions online through adaptive consumer decision support. Within this broad area, I have specialized in usable recommender systems and privacy decision-making.
In the area of recommender systems, I focus on usable mechanisms for users of such systems to input their preferences, and novel means to display and explain the resulting recommendations to users. An important goal I have in this area is to build systems that don’t just show users items that reflect their preferences, but help users better understand what their preferences are to begin with — systems I call “recommender systems for self-actualization.”
In the area of privacy decision-making, I focus on systems that actively assist consumers in their privacy decision-making practices — a concept I have dubbed “user-tailored privacy.” These systems should help users translate their privacy preferences into settings, thereby reducing the users’ burden of control while at the same time respecting their inherent privacy preferences.
Q: What inspired you to pursue your recent research project in improving ad experiences?
BK: Despite recent efforts to improve the user experience around online ads, there is a rise of distrust and skepticism around the collection and use of personal data for advertising purposes. There are a number of reasons for this distrust, including a lack of transparency and control. This lack of transparency and control not only generates mistrust, but also makes it more likely that the user models created by ad personalization algorithms reflect users’ immediate desires rather than their longer-term goals. The presented ads, in turn, tend to reflect these short-term likes, ignoring users’ ambitions and their better selves.
As someone who has worked extensively on transparency and control in both the field of recommender systems and the field of privacy, I am excited to apply this work to the area of ad experiences. In this project, my team therefore aims to design, build, and evaluate intuitive explanations of the ad recommendation process and interaction mechanisms that allow users to control this process. We will build these mechanisms in line with the nascent concepts of recommender systems for self-actualization and user-tailored privacy. The ultimate goal of this effort is to make advertisements more aligned with users’ long-term goals and ambitions.
Q: What were the results of this research?
BK: The work on this project is still very much ongoing. Our first step has been to conduct a systematic literature review on ad explanations, covering existing research on how they are generated, presented, and perceived by users. Based on this review, we developed a classification scheme that categorizes the existing literature on ad explanations offering insights into the reasoning behind the ad recommendation, the objective of the explanation, the content of the explanation, and how this content should be presented. This classification scheme offers a useful tool for researchers and practitioners to synthesize existing research on ad explanations and to identify paths for future research.
Our second step involves the development of a measurement instrument to evaluate ad experiences. The validation of this measurement instrument is still ongoing, but the end result will entail a carefully constructed set of questionnaires that can be used to users’ reactions toward online ads, including aspects of targeting accuracy, accountability, transparency, control, reliability, persuasiveness, and creepiness.
A third step involves a fundamental redesign of the ad experience on social networks, reimagining the very concept of advertising as a means to an end that serves the longer-term goals of the user. We are still in the very early stages of this activity, but we aim to explore the paradigm of recommendations, insights, and/or personal goals as a vehicle for this transformation of the ad experience.
Q: How has this research been received so far?
BK: Our paper on the literature review and the classification scheme of ad explanations was accepted to HICSS and was nominated as the Best Paper in the Social Media and e-Business Transformation minitrack. We are working on an interactive version of the classification scheme that provides a convenient overview of and direct access to the most relevant research in the area of ad explanations.
We are also working with Facebook researchers to make sure that our ad experience measurement instrument optimally serves their goal of creating a user-friendly ad experience.
Q: Where can people learn more about your research?
BK: You can find a project page about this research at www.usabart.nl/FBads. We will keep this page updated when new results become available!