Corpus ID: 11145400

Learning Case Feature Weights from Relevance and Ranking Feedback

  title={Learning Case Feature Weights from Relevance and Ranking Feedback},
  author={Luc Lamontagne and A. B. Guyard},
  booktitle={FLAIRS Conference},
  • Luc Lamontagne, A. B. Guyard
  • Published in FLAIRS Conference 2014
  • Computer Science
  • We study in this paper how explicit user feedback can be used by a case-based reasoning system to improve the quality of its retrieval phase. More specifically, we explore how both ranking feedback and relevance feedback can be exploited to modify the weights of case features. We propose some options to cope with each type of feedback. We also evaluate, in an interactive setting, their impact on a travel scenario where some user provides feedback on a series of queries. Our results indicate… CONTINUE READING

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    Publications referenced by this paper.
    Case-based recommender systems
    • 263
    • Open Access
    Learning Feature Weights from Case Order Feedback
    • 67
    Learning Feature Weights from Customer Return-Set Selections
    • 28
    • Open Access
    Advances in conversational case-based reasoning
    • D A V I, D W A H A, Q I A N G Y A N G
    • 2006
    • 39
    • Open Access
    Feedback, Case-Based Reasoning Research and Development, Springer
    • 1997
    Introduction to Information Retrieval
    • 5,312
    jCOLIBRI: Tutorial, Technical report, Facultad de Informatica, Universidad Complutense de Madrid, Spain
    • 2008