Learning Case Feature Weights from Relevance and Ranking Feedback

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 that the combined weight-learning scheme proposed in this paper succeeds, on average, to assign more weights to the features used to formulate relevance and ranking feedback.