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Similarity vs. Diversity

  • Barry Smyth
  • Paul McClave
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2080)

Abstract

Case-based reasoning systems usually accept the conventional similarity assumption during retrieval, preferring to retrieve a set of cases that are maximally similar to the target problem. While we accept that this works well in many domains, we suggest that in others it is misplaced. In particular, we argue that often diversity can be as important as similarity. This is especially true in case-based recommender systems. In this paper we propose and evaluate strategies for improving retrieval diversity in CBR systems without compromising similarity or efficiency.

Keywords

Recommender System Greedy Algorithm Retrieval Strategy Target Problem Greedy Selection 
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 2001

Authors and Affiliations

  • Barry Smyth
    • 1
    • 2
  • Paul McClave
    • 1
  1. 1.Smart Media InstituteUniversity College DublinDublinIreland
  2. 2.ChangingWorldsUniversity College DublinDublinIreland

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