Computer Science Department
School of Computer Science, Carnegie Mellon University
FALCON: Feedback Adaptive Loop for Context-based Retrieval
Leejay Wu, Christos Faloutsos, Katia Sycara, Terry R. Payne
We propose a novel method that is designed to handle disjunctive queries within metric spaces. The user provides weights for positive examples; our system ``learns'' the implied concept and returns similar objects. Our method differs from existing relevance-feedback methods that base themselves upon Euclidean or Mahalanobis metrics, as it facilitates learning even disjunctive, concave models within vector spaces, as well as arbitrary metric spaces.
Our main contributions are two-fold. Not only do we present a novel way to estimate the dissimilarity of an object to a set of desirable objects, but we support it with an algorithm that shows how to exploit metric indexing structures that support range queries to accelerate the search without incurring false dismissals. Our empirical results demonstrate that our method converges rapidly to excellent precision/recall, while outperforming sequential scanning by up to 200%.