2014/04/07 by Tien Thanh Nguyen, Pik-Mai Hui, F. Maxwell Harper +2 · 438 citations
Business, Management and Accounting · Computer Science · Engineering · Social Sciences · #Collaborative filtering #Computer science #Data mining #Digital Marketing and Social Media #Diversity (politics) #Engineering #Filter (signal processing) #FinTech, Crowdfunding, Digital Finance #Information retrieval #Measure (data warehouse) #Metric (unit) #Personalization #Recommender Systems and Techniques #Recommender system #Set (abstract data type) #Term (time) #Viewpoints #World Wide Web
paper · doi:10.1145/2566486.2568012
openalex publication_date 2014/04/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02
Eli Pariser coined the term 'filter bubble' to describe the potential for online personalization to effectively isolate people from a diversity of viewpoints or content. Online recommender systems - built on algorithms that attempt to predict which items users will most enjoy consuming - are one family of technologies that potentially suffers from this effect. Because recommender systems have become so prevalent, it is important to investigate their impact on users in these terms. This paper examines the longitudinal impacts of a collaborative filtering-based recommender system on users. To the best of our knowledge, it is the first paper to measure the filter bubble effect in terms of content diversity at the individual level. We contribute a novel metric to measure content diversity based on information encoded in user-generated tags, and we present a new set of methods to examine the temporal effect of recommender systems on the user experience. We do find that recommender systems expose users to a slightly narrowing set of items over time. However, we also see evidence that users who actually consume the items recommended to them experience lessened narrowing effects and rate items more positively.