Diversity in recommender systems - A survey

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How researchers see this paper

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Degenerate Feedback Loops in Recommender Systems (2019, background): “A signiﬁcant amount of research has therefore been devoted to deriving ways to favor diversity in the set of items an individual may be exposed to (see Kunaver and Porl (2017) for a review).”
Explaining machine learning classifiers through diverse counterfactual explanations (2019, methodology): “ending their work, we provide a method to construct a set of counterfactuals with diversity. In other domains of information search such as search engines and recommendation systems, multiple studies [15, 22, 35, 45] show the benefits of presenting a diverse set of information items to a user. Our h”
Algorithmic Fairness (2020, methodology): “It is interesting to note that P-fairness is somewhat related to the categorical diversity in recommender systems, requiring that recommendation lists are diverse [86, 92].”
Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks (2022, background): “81,82 Unfortunately, it is currently hard to measure diversity, because of not having multiple correct ground-truth answers per product molecule.”
Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “Hybrid RS approaches exploit the combination of two or more RS approaches (e.g. CF and CB) for generating an enhanced recommendation (Kunaver and Požrl 2017).”