Towards Transparency of Personalization on the Web

Lead PI

Co PIs

Abstract

This project will develop new research methods to map and quantify the ways in which online search engines, social networks, and e-commerce sites use sophisticated algorithms to tailor content to each individual user. This “personalization” may often be of value to the user, but it also has the potential to distort search results and manipulate the perceptions and behavior of the user. Given the popularity of personalization across a variety of Web-based services, this research has the potential for extremely broad impact. Being able to quantify the extent to which Web-based services are personalized will lead to greater transparency for users, and the development of tools to identify personalized content will allow users to access information that may be hard to access today.

Personalization is now a ubiquitous feature on many Web-based services. In many cases, personalization provides advantages for users because personalization algorithms are likely to return results that are relevant to the user. At the same time, the increasing levels of personalization in Web search and other systems are leading to growing concerns over the Filter Bubble effect, where users are only given results that the personalization algorithm thinks they want, while other important information remains inaccessible. From a computer science perspective, personalization is simply a tool that is applied to information retrieval and ranking problems. However, sociologists, philosophers, and political scientists argue that personalization can result in inadvertent censorship and “echo chambers.” Similarly, economists warn that unscrupulous companies can leverage personalization to steer users towards higher-priced products, or even implement price discrimination, charging different users different prices for the same item. As the pervasiveness of personalization on the Web grows, it is clear that techniques must be developed to understand and quantify personalization across a variety of Web services.

This research has four primary thrusts: (1) To develop methodologies to measure personalization of mobile content. The increasing popularity of browsing the Web from mobile devices presents new challenges, as these devices have access to sensitive content like the user’s geolocation and contacts. (2) To develop systems and techniques for accurately measuring the prevalence of several personalization trends on a large number of e-commerce sites. Recent anecdotal evidence has shown instances of problematic sales tactics, including price steering and price discrimination. (3) To develop techniques to identify and quantify personalized political content. (4) To measure the extent to which financial and health information is personalized based on location and socio-economic status. All four of these thrusts will develop new research methodologies that may prove effective in other areas of research as well.

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