User Privacy Concerns with Common Data Used in Recommender Systems

Abstract

Recommender systems, and personalization algorithms more broadly, have become an integral part of modern e-commerce, streaming, and social media services. Collaborative filtering in particular leverages users’ ratings to compute new items of interest. The algorithms that drive them use a variety of data, from user ratings to measures of social relationships. As a field, we have built more effective, accurate algorithms with the available data. However, recommender systems are often opaque to users, and users’ privacy concerns about the data these algorithms use is unknown.

In this project, we administered a survey to nearly 1,000 subjects to gauge their opinions about privacy issues tied to a variety of common personal data points used in making recommendations and the ways that data is used. We found that data collected within in an application is generally of low concern, while the use of social data and data obtained from third parties is often considered a privacy violation. Furthermore, users expressed discomfort with their data being used anonymously to help personalize content for others - a common practice in collaborative filtering. We discuss the survey results and implications for creating privacy-respecting recommender systems.