research-article

Stability of Recommendation Algorithms

Authors:

Gediminas Adomavicius,

Jingjing ZhangAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 30, Issue 4

Article No.: 23, Pages 1 - 31

https://doi.org/10.1145/2382438.2382442

Published: 01 November 2012 Publication History

Abstract

The article explores stability as a new measure of recommender systems performance. Stability is defined to measure the extent to which a recommendation algorithm provides predictions that are consistent with each other. Specifically, for a stable algorithm, adding some of the algorithm’s own predictions to the algorithm’s training data (for example, if these predictions were confirmed as accurate by users) would not invalidate or change the other predictions. While stability is an interesting theoretical property that can provide additional understanding about recommendation algorithms, we believe stability to be a desired practical property for recommender systems designers as well, because unstable recommendations can potentially decrease users’ trust in recommender systems and, as a result, reduce users’ acceptance of recommendations. In this article, we also provide an extensive empirical evaluation of stability for six popular recommendation algorithms on four real-world datasets. Our results suggest that stability performance of individual recommendation algorithms is consistent across a variety of datasets and settings. In particular, we find that model-based recommendation algorithms consistently demonstrate higher stability than neighborhood-based collaborative filtering techniques. In addition, we perform a comprehensive empirical analysis of many important factors (e.g., the sparsity of original rating data, normalization of input data, the number of new incoming ratings, the distribution of incoming ratings, the distribution of evaluation data, etc.) and report the impact they have on recommendation stability.

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Zhao SLiu Y(2024)Confidence region for distributed stochastic optimization problem via stochastic gradient tracking methodAutomatica10.1016/j.automatica.2023.111352159(111352)Online publication date: Jan-2024
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Xu JLiu JMa ZWang YWang WNgai E(2024)KNN-Based Collaborative Filtering for Fine-Grained Intelligent Grad-School Recommendation SystemQuality, Reliability, Security and Robustness in Heterogeneous Systems10.1007/978-3-031-65126-7_41(494-508)Online publication date: 20-Aug-2024
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Index Terms

Stability of Recommendation Algorithms
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 30, Issue 4

November 2012

216 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2382438

Issue’s Table of Contents

Copyright © 2012 ACM.

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Publication History

Published: 01 November 2012

Accepted: 01 June 2012

Revised: 01 March 2012

Received: 01 October 2011

Published in TOIS Volume 30, Issue 4

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