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 difficult 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 …

Metadata

publication

International Workshop on Algorithmic Bias in Search and Recommendation, 64-77, 2024

year

2024

publication date

2024/7/19

authors

Nathan Bartley, Keith Burghardt, Kristina Lerman

link

https://link.springer.com/chapter/10.1007/978-3-031-71975-2_5

resource_link

https://arxiv.org/pdf/2409.16558

book

International Workshop on Algorithmic Bias in Search and Recommendation

pages

64-77

publisher

Springer Nature Switzerland