article

An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering

Authors:

M. P. O'Mahony,

N. J. Hurley,

G. C. M. SilvestreAuthors Info & Claims

Artificial Intelligence Review, Volume 21, Issue 3-4

Pages 215 - 228

https://doi.org/10.1023/B:AIRE.0000036256.39422.25

Published: 01 June 2004 Publication History

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Abstract

Personalisation features are key to the success of many web applications and collaborative recommender systems have been widely implemented. These systems assist users in finding relevant information or products from the vast quantities that are frequently available. In previous work, we have demonstrated that such systems are vulnerable to attack and that recommendations can be manipulated. We introduced the concept of robustness as a performance measure, which is defined as the ability of a system to provide consistent predictions in the presence of noise in the data. In this paper, we expand on our previous work by examining the effects of several neighbourhood formation schemes and similarity measures on system performance. We propose a neighbourhood filtering mechanism for filtering false profiles from the neighbourhood in order to improve the robustness of the system.

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Cited By

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Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Guo SBai TDeng WFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Targeted Shilling Attacks on GNN-based Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615073(649-658)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615073
Aktukmak MYilmaz YUysal IBogers TSaid ABrusilovsky PTikk D(2019)Quick and accurate attack detection in recommender systems through user attributesProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347050(348-352)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347050
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Index Terms

An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering
1. Human-centered computing
  1. Collaborative and social computing
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 21, Issue 3-4

June 2004

211 pages

ISSN:0269-2821

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2004

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Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Guo SBai TDeng WFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Targeted Shilling Attacks on GNN-based Recommender SystemsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615073(649-658)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615073
Aktukmak MYilmaz YUysal IBogers TSaid ABrusilovsky PTikk D(2019)Quick and accurate attack detection in recommender systems through user attributesProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347050(348-352)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347050
Adomavicius GZhang J(2016)Classification, Ranking, and Top-K Stability of Recommendation AlgorithmsINFORMS Journal on Computing10.5555/3215270.321527228:1(129-147)Online publication date: 1-Feb-2016
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Tyagi SBharadwaj K(2014)A hybrid knowledge-based approach to collaborative filtering for improved recommendationsInternational Journal of Knowledge-based and Intelligent Engineering Systems10.3233/KES-14029218:2(121-133)Online publication date: 1-Apr-2014
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Gunes IKaleli CBilge APolat H(2014)Shilling attacks against recommender systemsArtificial Intelligence Review10.1007/s10462-012-9364-942:4(767-799)Online publication date: 1-Dec-2014
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Adomavicius GZhang J(2012)Stability of Recommendation AlgorithmsACM Transactions on Information Systems10.1145/2382438.238244230:4(1-31)Online publication date: 1-Nov-2012
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Hurley NMobasher BBurke RJannach DAdomavicius G(2011)Robustness of recommender systemsProceedings of the fifth ACM conference on Recommender systems10.1145/2043932.2043937(9-10)Online publication date: 23-Oct-2011
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Dell'Amico MCapra L(2010)Dependable filteringACM Transactions on Information Systems10.1145/1877766.187777129:1(1-37)Online publication date: 27-Dec-2010
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Hurley NCheng ZZhang MBergman LTuzhilin ABurke RFelfernig ASchmidt-Thieme L(2009)Statistical attack detectionProceedings of the third ACM conference on Recommender systems10.1145/1639714.1639740(149-156)Online publication date: 23-Oct-2009
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Cited By

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Iterative Neighbourhood Similarity Computation for Collaborative Filtering

Trust-based collaborative filtering: tackling the cold start problem using regular equivalence

The efficient imputation method for neighborhood-based collaborative filtering

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