Personalization Research @ Northeastern

This site is the homepage for the Personalization Research Group within the College of Computer and Information Science at Northeastern University. Here, you will find explanations of and links to our work, as well as open-source data and code from our research.

Why Study Personalization on the Web?

Today, many major websites personalize the content that they show to users. Examples include: Google Search, which personalizes search results to try and surface more relevant content; Amazon and Netflix, which personalize product and movie recommendations; and Facebook, which personalizes each user's news-feed to highlight engaging content. The proliferation of personalization on the Web is driven by the explosion of Big Data that is available about people's online and offline behavior.

Although there are cases where personalization is beneficial to users, scientists and regulators have become increasingly concerned that personalization may also harm Web users. For example, sociologists and political scientists are concerned that online Filter Bubbles may create "echo chambers" that increase political polarization. Similarly, personalization on e-commerce sites can be used to implement price discrimination.

Given the enormous number of people who rely on the Web, it is imperative that we understand how personalization algorithms are being deployed, and the effect that they have on Web users. Below, you will find links to specific research projects that our group has undertaken to address these issues.

Web Search

Billions of people around the world rely on Google Search as their gateway to information on the Web. In this project, we examine how Google Search personalizes results for users, and what types of search queries are more heavily personalized.

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Price Discrimination

On the web, it is possible for e-commerce sites to personalize the prices of products for each person, a phenomenon known as price discrimination. In this project, we measures personalization on major e-commerce and travel sites to identify cases of price discrimination, as well as a related technique called price steering.

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