We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial documen...
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
A major obstacle that decreases the performance of text classifiers is the extremely high dimensionality of text data. To reduce the dimension, a number of approaches based on rou...
LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the L...