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 Alexandre Bergeron Guyard},
  booktitle={FLAIRS Conference},
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… Expand
Learning and Engineering Similarity Functions for Business Recommenders and Clustering
  • H. Witschel, A. Martin
  • Computer Science
  • AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering
  • 2019
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The maintenance methodology presented in this paper deals with numerical recommendations and can manage a potentially huge case-base due to the combinatorial derived from the number of attributes used to represent a case. Expand
A prototype that supports the execution of the Intelligence Preparation of the Operational Environment (IPOE) and its reasoning processes, and the learning mechanisms used to improve the case-based reasoning are described. Expand


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A new framework for learning similarity measures is presented, whose main advantage is its generality, because its application is not restricted to classification tasks in contrast to other already known algorithms. Expand
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This paper describes a comprehensive set of techniques for learning local feature weights and evaluates these techniques on a case-base for conflict resolution in air traffic control and shows how introspective learning of feature weights improves retrieval and how it can be used to determine context sensitive local weights. Expand
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  • K. Branting
  • Computer Science
  • Knowledge and Information Systems
  • 2003
LCW’s estimate of the mean preferences of a customer population improved as the number of customers increased, even for larger numbers of features of widely differing importance, which led to improved prediction of individual customers’ rankings. Expand
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This paper describes LCW, a procedure for learning customer preferences represented as feature weights by observing customers' selections from return sets. An empirical evaluation on simulatedExpand
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A class of weight-setting methods for lazy learning algorithms which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings. Expand
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A framework in which these systems can be understood is defined, and a selection of papers from the case-based recommender systems literature is reviewed, covering the development of these systems over the last ten years. Expand
Advances in conversational case-based reasoning
A considerable amount of research in case-based reasoning (CBR) has recently focused on conversational CBR as a means of providing more effective support for interactive problem solving. We reviewExpand
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  • Computer Science
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