Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models

TL;DR

This paper presents scalable, exact top-K inference algorithms for separable linear relational models, applicable across various machine learning tasks, significantly improving efficiency over naive methods.

Contribution

It introduces a unified framework for exact top-K inference using modified information retrieval algorithms in multiple machine learning contexts.

Findings

Threshold algorithm is highly scalable and efficient.

Exact top-K retrieval is possible with incomplete scoring.

Experimental results show orders of magnitude speedup.

Abstract

Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly. Examples of such problems emerge in several subfields of machine learning, such as collaborative filtering, multi-label classification, dyadic prediction and biological network inference. In this article we analyse efficient and exact algorithms for computing the top-$K$ predictions in the above problem settings, using a general class of models that we refer to as separable linear relational models. We show how to use those inference algorithms, which are modifications of well-known information retrieval methods, in a variety of machine learning settings. Furthermore, we study the possibility of scoring items incompletely, while still retaining an exact top-K retrieval. Experimental results in several application domains reveal that the so-called threshold algorithm is very scalable, performing often many orders of magnitude more efficiently than the naive approach.