Metric information filtering

The traditional problem of similarity search requires to find, within a set of points, those that are closer to a query point q, according to a distance function d. In this paper we introduce the novel problem of metric information filtering (MIF): in this scenario, each point xi comes with its own distance function di and the task is to efficiently determine those points that are close enough, according to di, to a query point q. MIF can be seen as an extension of both the similarity search problem and of approaches currently used in content-based information filtering, since in MIF user profiles (points) and new items (queries) are compared using arbitrary, personalized, metrics. We introduce the basic concepts of MIF and provide alternative resolution strategies aiming to reduce processing costs. Our experimental results show that the proposed solutions are indeed effective in reducing evaluation costs.

Research Highlights
► Basic principles of information filtering in metric spaces (MIF).
► Pivot-based resolution strategies for Lipschitz-equivalent metrics.
► Experimental assessment of the efficiency of the proposed techniques.