Direct Optimization of Ranking Measures

TL;DR

This paper introduces a novel method for directly optimizing ranking performance measures using structured estimation in Hilbert spaces, enabling efficient training and effective ranking in web page and collaborative filtering tasks.

Contribution

It presents a new approach that directly optimizes ranking measures via structured estimation, overcoming limitations of pairwise methods like SVMs.

Findings

The algorithm is fast during training and testing.

It effectively optimizes multivariate performance measures.

The method outperforms existing approaches in experiments.

Abstract

Web page ranking and collaborative filtering require the optimization of sophisticated performance measures. Current Support Vector approaches are unable to optimize them directly and focus on pairwise comparisons instead. We present a new approach which allows direct optimization of the relevant loss functions. This is achieved via structured estimation in Hilbert spaces. It is most related to Max-Margin-Markov networks optimization of multivariate performance measures. Key to our approach is that during training the ranking problem can be viewed as a linear assignment problem, which can be solved by the Hungarian Marriage algorithm. At test time, a sort operation is sufficient, as our algorithm assigns a relevance score to every (document, query) pair. Experiments show that the our algorithm is fast and that it works very well.