Improving stability of recommender systems: A meta-algorithmic approach

Gediminas Adomavicius, Jingjing Zhang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

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 iterative smoothing - that can be used in conjunction with different traditional recommendation algorithms to improve their stability. Our experimental results on real-world rating data demonstrate that both approaches can achieve substantially higher stability as compared to the original recommendation algorithms. Furthermore, perhaps as importantly, the proposed approaches not only do not sacrifice the predictive accuracy in order to improve recommendation stability, but are actually able to provide additional accuracy improvements.

Original languageEnglish (US)
Article number6994303
Pages (from-to)1573-1587
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume27
Issue number6
DOIs
StatePublished - Jun 1 2015

Keywords

  • Recommender systems
  • bagging
  • collaborative filtering
  • iterative smoothing
  • recommendation stability

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