Human Interaction with Recommendation Systems: On Bias and Exploration

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How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, methodology): “We draw on a model recently proposed by Schmit and Riquelme [54] that captures the interaction between recommended items and users, and we modify this model to allow for personalized recommendations and multiple interactions for a given user.1 Definition 1.”
Feedback Loop and Bias Amplification in Recommender Systems (2020, background): “recommender systems has recently received more attention from researchers [4, 5, 9, 14–16].”