Maximizing stability of recommendation algorithms: A collective inference approach

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Stable and Fair Classification (2019, background): “Moreover, stability is also important for the reliability and acceptability of recommendation systems [2, 1, 3].”
Classification, Ranking, and Top-K Stability of Recommendation Algorithms (2016, background): “Furthermore, it has been demonstrated that some widely used collaborative filtering recommendation algorithms suffer from a high degree of instability (Adomavicius and Zhang 2010, 2012a), and new general-purpose approaches have been proposed that can improve stability for a variety of recommendation”