Structured Ranking Learning using Cumulative Distribution Networks

by Jim C. Huang and Brendan J. Frey

This NIPS paper further elucidates how to take advantage of CDN's idea to tackle a ranking problem. The author introduce a probability distributon for the preference variable given an ordered relationship. Therefore a natural choice of the ranking scheme is to maximize the likelihood or equivalently to minimize the negative log-likelihood (loss function). Once the ranking function could be parameterized, the optimization problem could be solved. The paper employs a nonparametric model. CDN here plays the role of setting up the CDF for the observed preference variable.

With this formulation, several earlier proposed algorithms could be regarded its special cases, such as RankNet, ListNet and ListMLE. I will take a close look at those ranking algorithms later.