Bursting your (filter) bubble: strategies for promoting diverse exposure

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How researchers see this paper

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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “This has important societal implications as it can lead to less diverse exposure to content, or to being less exposed to content that challenges one’s views (Resnick et al., 2013).”
Governance of algorithms: options and limitations (2015, methodology): “Services such as ConsiderIt, Reflect and OpinionSpace are designed to avoid filter bubbles and bias by integrating elements of serendipity (Munson and Resnick, 2010; Schedl et al., 2012; Resnick et al., 2013).”
Managing Popularity Bias in Recommender Systems with Personalized Re-ranking (2019, background): “Result diversiﬁcation has been studied in the context of information retrieval, especially for web search engines, which have a similar goal to ﬁnd a ranking of documents that together provide a complete coverage of the aspects underlying a query (Santos et al. 2015).”
Controlling Popularity Bias in Learning-to-Rank Recommendation (2017, background): “Systems that use active learning to explore each user’s prole will typically need to present more long tail items because these are the ones that the user is less likely to have rated, and where user’s preferences are more likely to be diverse [12, 15].”