Improving Stability of Recommender Systems: A Meta-Algorithmic Approach

@article{Adomavicius2015ImprovingSO,
  title={Improving Stability of Recommender Systems: A Meta-Algorithmic Approach},
  author={Gediminas Adomavicius and Jingjing Zhang},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2015},
  volume={27},
  pages={1573-1587}
}
This paper focuses on the measure of recommendation stability, which reflects the consistency of recommender system predictions. Stability is a desired property of recommendation algorithms and has important implications on users' trust and acceptance of recommendations. Prior research has reported that some popular recommendation algorithms can suffer from a high degree of instability. In this study, we explore two scalable, general-purpose meta-algorithmic approaches-based on bagging and… CONTINUE READING

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