Listwise approach to learning to rank: theory and algorithm

This paper aims to conduct a study on the listwise approach to learning to rank. The listwise approach learns a ranking function by taking individual lists as instances and minimizing a loss function defined on the predicted list and the ground-truth list. Existing work on the approach mainly focused on the development of new algorithms; methods such as RankCosine and ListNet have been proposed and good performances by them have been observed. Unfortunately, the underlying theory was not sufficiently studied so far. To amend the problem, this paper proposes conducting theoretical analysis of learning to rank algorithms through investigations on the properties of the loss functions, including consistency, soundness, continuity, differentiability, convexity, and efficiency. A sufficient condition on consistency for ranking is given, which seems to be the first such result obtained in related research. The paper then conducts analysis on three loss functions: likelihood loss, cosine loss...

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, Hang Li
• Keywords: Cross Entropy Loss, ICML 2008, Likelihood Loss, Loss Function, Machine Learning