Accession Number:

ADA517738

Title:

Northeastern University in TREC 2009. Million Query Track

Descriptive Note:

Conference paper

Corporate Author:

SHEFFIELD UNIV (UNITED KINGDOM)

Report Date:

2009-11-01

Pagination or Media Count:

7.0

Abstract:

Ranking is a central problem in information retrieval. Modern search engines, especially those designed for the World Wide Web, commonly analyze and combine hundreds of features extracted from the submitted query and underlying documents in order to assess the relative relevance of a document to a given query and thus rank the underlying collection. The sheer size of this problem has led to the development of learning to rank LTR algorithms that can automate the construction of such ranking functions Given a training set of feature vector, relevance pairs, a machine learning procedure learns how to combine the query and document features in such a way so as to effectively assess the relevance of any document to any query and thus rank a collection in response to a user input. Much thought and research has been placed on the development of sophisticated learning-to-rank algorithms. However, relatively little research has been conducted on the construction of appropriate learning to rank data sets nor on the effect of these data sets on the ability of a learning-to-rank algorithm to learn effectively.

Subject Categories:

  • Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE