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Dynamic Critiquing

  • James Reilly
  • Kevin McCarthy
  • Lorraine McGinty
  • Barry Smyth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3155)

Abstract

Critiquing is a powerful style of feedback for case-based recommender systems. Instead of providing detailed feature values, users indicate a directional preference for a feature. For example, a user might ask for a ‘less expensive’ restaurant in a restaurant recommender; ‘less expensive’ is a critique over the price feature. The value of critiquing is that it is generally applicable over a wide range of domains and it is an effective means of focusing search. To date critiquing approaches have usually been limited to single-feature critiques, and this ultimately limits the degree to which a given critique can eliminate unsuitable cases. In this paper we propose extending the critiquing concept to cater for the possibility of compound critiques – critiques over multiple case features. We describe a technique for automatically generating useful compound critiques and demonstrate how this can significantly improve the performance of a conversational recommender system. We also argue that this generalised form of critiquing offers explanatory benefits by helping the user to better understand the structure of the recommendation space.

Keywords

Recommender System Frequent Itemsets Session Length Dynamic Critique Target Case 
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 2004

Authors and Affiliations

  • James Reilly
    • 1
  • Kevin McCarthy
    • 1
  • Lorraine McGinty
    • 1
  • Barry Smyth
    • 1
  1. 1.Adaptive Information Cluster, Smart Media Institute, Department of Computer ScienceUniversity College Dublin (UCD)Ireland

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