Hierarchical Classification by Expected Utility Maximization

Abstract

Hierarchical classification refers to an extension of the standard classification problem, in which labels must be chosen from a class hierarchy. In this paper, we look at hierarchical classification from an information retrieval point of view. More specifically, we consider a scenario in which a user searches a document in a topic hierarchy. This scenario gives rise to the problem of predicting an optimal entry point, that is, a topic node in which the user starts searching. The usefulness of a corresponding prediction strongly depends on the search behavior of the user, which becomes relevant if the document is not immediately found in the predicted node. Typically, users tend to browse the hierarchy in a top-down manner, i.e., they look at a few more specific subcategories but usually refuse exploring completely different branches of the search tree. From a classification point of view, this means that a prediction should be evaluated, not solely on the basis of its correctness, but rather by judging its usefulness against the background of the user behavior. The idea of this paper is to formalize hierarchical classification within a decision-theoretic framework which allows for modeling this usefulness in terms of a user-specific utility function. The prediction problem thus becomes a problem of expected utility maximization. Apart from its theoretical appeal, we provide first empirical results showing that the approach performs well in practice.