Adaptable Similarity Search using Non-Relevant Information

Abstract

This chapter presents a novel technique for improving the accuracy of adaptable similarity based retrieval by incorporating negative relevance judgment, and demonstrates excellent performance and robustness of the proposed scheme with a large number of experiments. Many modern database applications require content-based similarity search capability in numeric attribute space. Therefore, online techniques for adaptively refining the similarity metric based on relevance feedback from the user are necessary. Existing methods use retrieved items marked relevant by the user to refine the similarity metric, without taking into account the information about non-relevant (or unsatisfactory) items. Consequently, items in database close to non-relevant ones continue to be retrieved in further iterations. A decision surface is determined to split the attribute space into relevant and non-relevant regions. The decision surface is composed of hyperplanes, each of which is normal to the minimum distance vector from a non-relevant point to the convex hull of the relevant points.