Learning to Rank for Information Retrieval

  • Tie-Yan Liu

Table of contents

  1. Front Matter
    Pages I-XVII
  2. Overview of Learning to Rank

    1. Front Matter
      Pages 1-1
    2. Tie-Yan Liu
      Pages 3-30
  3. Major Approaches to Learning to Rank

    1. Front Matter
      Pages 31-31
    2. Tie-Yan Liu
      Pages 33-47
    3. Tie-Yan Liu
      Pages 49-70
    4. Tie-Yan Liu
      Pages 71-88
    5. Tie-Yan Liu
      Pages 89-99
  4. Advanced Topics in Learning to Rank

    1. Front Matter
      Pages 101-101
    2. Tie-Yan Liu
      Pages 103-111
    3. Tie-Yan Liu
      Pages 113-121
    4. Tie-Yan Liu
      Pages 123-126
    5. Tie-Yan Liu
      Pages 127-130
  5. Benchmark Datasets for Learning to Rank

    1. Front Matter
      Pages 131-131
    2. Tie-Yan Liu
      Pages 133-143
    3. Tie-Yan Liu
      Pages 145-152
    4. Tie-Yan Liu
      Pages 153-155
  6. Practical Issues in Learning to Rank

    1. Front Matter
      Pages 157-157
    2. Tie-Yan Liu
      Pages 159-179
    3. Tie-Yan Liu
      Pages 181-191

About this book

Introduction

Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people.

The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”.

Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance.

This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.

Keywords

Information Retrieval Machine Learning Ranking Algorithms Statistical Learning

Authors and affiliations

  • Tie-Yan Liu
    • 1
  1. 1.Microsoft Research AsiaHaidian District, BeijingChina, People's Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-14267-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-14266-6
  • Online ISBN 978-3-642-14267-3