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The prevalence of algorithmic recommendations has raised public concern about undesired societal effects. A central threat is the risk of polarization, which is difficult to conceptualize and to measure, making it difficult to assess the role of Recommender Systems in this phenomenon.

These difficulties have yielded two types of analyses: 1) purely topological approaches that study how recommenders isolate or connect types of nodes in a graph, and 2) spatial opinion approaches that study how recommenders change the distribution of users on a given opinion scale. The former analyses prove i... [+]