Abstract

Online social networks use recommender systems to suggest relevant information to their users in the form of personalized timelines. Studying how these systems expose people to information at scale is diffi-cult to do as one cannot assume each user is subject to the same timeline condition and building appropriate evaluation infrastructure is costly. We show that a simple agent-based model where users have fixed preferences affords us the ability to compare different recommender systems (and thus different personalized timelines) in their ability to skew users' perception of their network. Importantly, we show that a simple greedy algorithm that constructs a feed based on network properties reduces such perception biases comparable to a random feed. This underscores the influence network structure has in determining the effectiveness of recommender systems in the social network context and offers a tool for mitigating perception biases through algorithmic feed construction.

Metadata

publication

ADVANCES IN BIAS AND FAIRNESS IN INFORMATION RETRIEVAL: 5th, 64, 2024

year

2024

publication date

2025

authors

Nathan Bartley, Keith Burghardt

link

https://books.google.com/books?hl=en&lr=&id=WqMrEQAAQBAJ&oi=fnd&pg=PA64&dq=info:B7LVXuDficEJ:scholar.google.com&ots=QHO3aBeQyB&sig=VsMzw7oFvHLypwryjeLhlSOEZF0

journal

Advances in Bias and Fairness in Information Retrieval: 5th International Workshop, BIAS 2024, Washington, DC, USA, July 18, 2024, Revised Selected Papers

volume

2227

pages

64

publisher

Springer Nature