Distance-based Positive and Unlabeled Learning for Ranking

TL;DR

This paper introduces a novel ranking method that leverages positive and unlabeled data using distance metrics and integer linear programming, effective even with minimal supervision, demonstrated in graph vertex nomination.

Contribution

It proposes a model-agnostic distance-based ranking approach utilizing ILP with limited supervision, applicable to various domains including graph vertex nomination.

Findings

Effective ranking with minimal supervision demonstrated

Method outperforms traditional approaches in simulations

Applicable to real-world graph data

Abstract

Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest. The setting we consider is one in which no analytic description of what constitutes a good ranking is available. Instead, we have a collection of representations and supervisory information consisting of a (target item, interesting items set) pair. We demonstrate analytically, in simulation, and in real data examples that learning to rank via combining representations using an integer linear program is effective when the supervision is as light as "these few items are similar to your item of interest." While this nomination task is quite general, for specificity we present our methodology from the perspective of vertex nomination in graphs. The methodology described herein is model agnostic.