article

Collaborative recommendation: A robustness analysis

Authors:

Michael O'Mahony,

Nicholas Kushmerick,

Guénolé SilvestreAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 4, Issue 4

Pages 344 - 377

https://doi.org/10.1145/1031114.1031116

Published: 01 November 2004 Publication History

Abstract

Collaborative recommendation has emerged as an effective technique for personalized information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. To explore this issue, we analyse the <i>robustness</i> of collaborative recommendation: the ability to make recommendations despite (possibly intentional) noisy product ratings. There are two aspects to robustness: recommendation <i>accuracy</i> and <i>stability</i>. We formalize recommendation accuracy in machine learning terms and develop theoretically justified models of accuracy. In addition, we present a framework to examine recommendation stability in the context of a widely-used collaborative filtering algorithm. For each case, we evaluate our analysis using several real-world data-sets. Our investigation is both practically relevant for enterprises wondering whether collaborative recommendation leaves their marketing operations open to attack, and theoretically interesting for the light it sheds on a comprehensive theory of collaborative recommendation.

References

[1]

Albert, M. and Aha, D. 1991. Analyses of instance-based learning algorithms. In Proceedings of the 9th National Conference on Artificial Intelligence.]]

[2]

Angluin, D. and Laird, P. 1988. Learning from noisy examples. Machine Learning 2, 4, 343--370.]]

Digital Library

[3]

Azar, Y., Fiat, A., Karlin, A., McSherry, F., and Saia, J. 2001. Spectral analysis of data. In Proceedings of the 32nd ACM Symposium on the Theory of Computing.]]

Digital Library

[4]

Breese, J., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Conference on the Uncertainty in Artificial Intelligence.]]

Digital Library

[5]

Drineas, P., Kerenidis, I., and Raghavan, P. 2002. Competetive recommender systems. In Proceedings of the 32nd ACM Symposium on the Theory of Computing.]]

Digital Library

[6]

Goldberg, D., Nichols, D., Oki, B., and Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Comm. ACM 35, 12, 61--70.]]

Digital Library

[7]

Groot, P., van Harmelen, F., and ten Teije, A. 2000. Torture tests: a quantitative analysis for the robustness of knowledge-based systems. In European Workshop on Knowledge Acquisition, Modelling and Management (EKAW'00). LNAI Springer-Verlag.]]

Digital Library

[8]

Haussler, D. 1990. Probably approximately correct learning. In National Conference on Artificial Intelligence. 1101--1108.]]

[9]

Herlocker, J., Konstan, J., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of ACM SIGIR'99.]]

Digital Library

[10]

Hsu, C.-N. and Knoblock, C. A. 1995. Estimating the robustness of discovered knowledge. In Proceedings of the First International Conference on Knwledge Discovery and Data Mining. Montreal, Canada.]]

[11]

Hsu, C.-N. and Knoblock, C. A. 1996. Discovering robust knowledge from dynamic closed-world data. In Proceedings of AAAI'96.]]

Digital Library

[12]

http://movielens.umn.edu.]]

[13]

http://www.changingworlds.com.]]

[14]

IEEE. 1990. IEEE standard glossary of software engineering terminology. IEEE Standard 610.12-1990.]]

[15]

Kearns, M. and Li, M. 1988. Learning in the presence of malicious errors. In Proceedings of the ACM Symposium on the Theory of Computing.]]

Digital Library

[16]

Kleinberg, J. 1998. Authoritative sources in a hyperlinked environment. In Proceedings of the ACM SIAM Symposium on Discrete Algorithms.]]

Digital Library

[17]

Kumar, R., Raghavan, P., Rajagopalan, S., and Tompkins, A. 1998. Recommender systems: A probabilistic analysis. In Proceedings of the 39th IEEE Symposium on the Foundations of Computer Science.]]

Digital Library

[18]

Ng, A., Zheng, A., and Jordan, M. 2001. Link analysis, eigenvectors and stability. In Proceedings of the 17th International Joint Conference on Artificial Intelligence.]]

Digital Library

[19]

Pennock, D., Horvitz, E., and Giles, L. 2000. Socal choice theory and recommender systems: Analysis of the axiomatic foundations of collabortative filtering. In National Conference on Artificial Intelligence. 729--734.]]

Digital Library

[20]

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. An open architecture for collaborative filtering of netnews. In ACM Conference on Computer Supported Cooperative Work.]]

Digital Library

[21]

Shardanand, U. and Maes, P. 1994. Social information filtering: Algorithms for automating "word of mouth". In Proceedings of the Conference on Human Factors in Computing Systems.]]

Digital Library

[22]

Valiant, L. 1984. A theory of the learnable. Comm. ACM, 1134--1142.]]

Digital Library

Cited By

Gunes I(2024)A novel clustered-based detection method for shilling attack in private environmentsPeerJ Computer Science10.7717/peerj-cs.213710(e2137)Online publication date: 24-Jun-2024
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Li MLian YZhu JLin JWan JSun Y(2024)A Sampling-Based Method for Detecting Data Poisoning Attacks in Recommendation SystemsMathematics10.3390/math1202024712:2(247)Online publication date: 12-Jan-2024
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Duan MLi KZhang WQin JXiao B(2024)Attacking Click-through Rate Predictors via Generating Realistic Fake SamplesACM Transactions on Knowledge Discovery from Data10.1145/364368518:5(1-24)Online publication date: 28-Feb-2024
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Index Terms

Collaborative recommendation: A robustness analysis
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 4, Issue 4

November 2004

108 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/1031114

Issue’s Table of Contents

Copyright © 2004 ACM.

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Publication History

Published: 01 November 2004

Published in TOIT Volume 4, Issue 4

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Gunes I(2024)A novel clustered-based detection method for shilling attack in private environmentsPeerJ Computer Science10.7717/peerj-cs.213710(e2137)Online publication date: 24-Jun-2024
https://doi.org/10.7717/peerj-cs.2137
Li MLian YZhu JLin JWan JSun Y(2024)A Sampling-Based Method for Detecting Data Poisoning Attacks in Recommendation SystemsMathematics10.3390/math1202024712:2(247)Online publication date: 12-Jan-2024
https://doi.org/10.3390/math12020247
Duan MLi KZhang WQin JXiao B(2024)Attacking Click-through Rate Predictors via Generating Realistic Fake SamplesACM Transactions on Knowledge Discovery from Data10.1145/364368518:5(1-24)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3643685
Lin CChen SZeng MZhang SGao MLi H(2024)Shilling Black-Box Recommender Systems by Learning to Generate Fake User ProfilesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318321035:1(1305-1319)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3183210
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Hamidi HMoradi R(2024)Design of a dynamic and robust recommender system based on item context, trust, rating matrix and rating time using social networks analysisJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10196436:2(101964)Online publication date: Feb-2024
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Paul AWan YChen BWu Z(2024)SPERM: sequential pairwise embedding recommendation with MI-FGSMInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02288-zOnline publication date: 19-Jul-2024
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