Exposure Inequality in People Recommender Systems: The Long-Term Effects
Francesco Fabbri, Maria Luisa Croci, Francesco Bonchi, Carlos Castillo

TL;DR
This paper models how link recommendation algorithms in social networks can create long-term exposure inequalities, favoring homophilic minority groups and amplifying disparities through feedback loops.
Contribution
Introduces a simulation model to analyze the long-term effects of various link recommenders on exposure inequality in social networks.
Findings
Homophilic minority groups gain disproportionate exposure.
Heterophilic minority groups experience under-exposure.
Recommenders amplify the 'rich-get-richer' effect.
Abstract
People recommender systems may affect the exposure that users receive in social networking platforms, influencing attention dynamics and potentially strengthening pre-existing inequalities that disproportionately affect certain groups. In this paper we introduce a model to simulate the feedback loop created by multiple rounds of interactions between users and a link recommender in a social network. This allows us to study the long-term consequences of those particular recommendation algorithms. Our model is equipped with several parameters to control (i) the level of homophily in the network, (ii) the relative size of the groups, (iii) the choice among several state-of-the-art link recommenders, and (iv) the choice among three different user behavior models, that decide which recommendations are accepted or rejected. Our extensive experimentation with the proposed model shows that a…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Social Media and Politics
