Iterative smoothing technique for improving stability of recommender systems

Gediminas Adomavicius, Jingjing Zhang

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


We focus 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 propose a scalable, general-purpose iterative smoothing approach 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 the proposed approach can achieve substantially higher stability as compared to the original recommendation algorithms. Importantly, the proposed approach not only does not sacrifice the predictive accuracy in order to improve recommendation stability, but is actually able to provide additional accuracy improvements at the same time.

Original languageEnglish (US)
Pages (from-to)3-8
Number of pages6
JournalCEUR Workshop Proceedings
StatePublished - Dec 1 2012
EventWorkshop on Recommendation Utility Evaluation: Beyond RMSE, RUE 2012 - Workshop at the 6th ACM International Conference on Recommender Systems, RecSys 2012 - Dublin, Ireland
Duration: Sep 9 2012Sep 9 2012


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