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Abstract

We present techniques for the discovery of recommendation rules that describe the behaviour of a recommender system in localised areas of the product space. Potential uses of the discovered rules include assessing the performance of the system in terms of recommendation efficiency and solution quality. For example, the discovered rules may reveal potential efficiency gains that might be achieved with an alternative recommendation strategy. We also present an efficient algorithm for automating the discovery of recommendation rules in nearest-neighbour (NN) retrieval, the standard approach to product recommendation in case-based reasoning (CBR).

Keywords

Recommender System Product Space Importance Weight Product Recommendation Recommendation Strategy 
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 London Limited 2005

Authors and Affiliations

  • David McSherry
    • 1
  1. 1.School of Computing and Information EngineeringUniversity of UlsterColeraineNorthern Ireland

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