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Robustness Analyses of Instance-Based Collaborative Recommendation

  • Nicholas Kushmerick
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2430)

Abstract

Collaborative recommendation has emerged as an effective technique for a personalized information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. We analyze the robustness of collaborative recommendation: the ability to make recommendations despite (possibly intentional) noisy product ratings. We formalize robustness in machine learning terms, develop two theoretically justified models of robustness, and evaluate the models on real-world data. 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.

Keywords

Recommender System Absolute Accuracy Robustness Analysis Target Concept Noise Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Nicholas Kushmerick
    • 1
  1. 1.Computer Science DepartmentUniversity College DublinDublin

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