Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking
model using training data, such that the model can sort new objects according to their …

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 …

In this paper we address the issue of learning to rank for document retrieval. In the task, a
model is automatically created with some training data and then is utilized for ranking of …

This paper is concerned with learning to rank for information retrieval (IR). Ranking is the
central problem for information retrieval, and employing machine learning techniques to …

The Markov Random Walk model has been recently exploited for multi-document
summarization by making use of the link relationships between sentences in the document …

Ranking is a very important topic in information retrieval. While algorithms for learning
ranking models have been intensively studied, this is not the case for feature selection …

Web graphs are approximate snapshots of the web, created by search engines. They are
essential to monitor the evolution of the web and to compute global properties like …

Ranking problem is becoming important in many fields, especially in information retrieval
(IR). Many machine learning techniques have been proposed for ranking problem, such as …

Many machine learning technologies such as support vector machines, boosting, and neural
networks have been applied to the ranking problem in information retrieval. However, since …

One central problem of information retrieval (IR) is to determine which documents are
relevant and which are not to the user information need. This problem is practically handled …