Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
We consider the task of devising large-margin based surrogate losses for the learning to rank problem. In this learning to rank setting, the traditional hinge loss for structured ...
Ranking is at the heart of many information retrieval applications. Unlike standard regression or classification in which we predict outputs independently, in ranking we are inter...
Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations, rather than single values. In the recent ...
Abstract. Machine learning ranking methods are increasingly applied to ranking tasks in information retrieval (IR). However ranking tasks in IR often differ from standard ranking t...