Efficient top-n recommendation for very large scale binary rated datasets

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Recommender Systems (2016, unknown): “The extension of neighborhood methods to very large-scale data sets has been studied in [51].”
Embarrassingly Shallow Autoencoders for Sparse Data (2019, background): “While model-based approaches were found to achieve be er ranking accuracy on some data sets, neighborhood-based approaches dominated on others, e.g., the Million Song Data Competition on Kaggle [1, 14].”
Current challenges and visions in music recommender systems research (2017, methodology): “is variation of MAP was popularized by Kaggle competitions [1] about recommender systems and has been used in several other research works, consider for example [9, 124].”