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Context-Aware Ranking with Factorization Models

  • Steffen Rendle

Part of the Studies in Computational Intelligence book series (SCI, volume 330)

Table of contents

  1. Front Matter
  2. Overview

    1. Front Matter
      Pages 1-1
    2. Steffen Rendle
      Pages 3-8
    3. Steffen Rendle
      Pages 9-15
  3. Theory

    1. Front Matter
      Pages 17-17
    2. Steffen Rendle
      Pages 19-37
    3. Steffen Rendle
      Pages 39-50
    4. Steffen Rendle
      Pages 51-65
  4. Application

    1. Front Matter
      Pages 67-68
    2. Steffen Rendle
      Pages 69-84
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      Pages 85-111
    4. Steffen Rendle
      Pages 113-133
  5. Extensions

    1. Front Matter
      Pages 135-136
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      Pages 137-153
    3. Steffen Rendle
      Pages 155-170
  6. Conclusion

    1. Front Matter
      Pages 171-171
    2. Steffen Rendle
      Pages 173-176
  7. Back Matter

About this book

Introduction

Context-aware ranking is an important task with many applications. E.g. in recommender systems items (products, movies, ...) and for search engines webpages should be ranked. In all these applications, the ranking is not global (i.e. always the same) but depends on the context. Simple examples for context are the user for recommender systems and the query for search engines. More complicated context includes time, last actions, etc. The major problem is that typically the variable domains (e.g. customers, products) are categorical and huge, the observations are very sparse and only positive events are observed. In this book, a generic method for context-aware ranking as well as its application are presented. For modelling a new factorization based on pairwise interactions is proposed and compared to other tensor factorization approaches. For learning, the `Bayesian Context-aware Ranking' framework consisting of an optimization criterion and algorithm is developed. The second main part of the book applies this general theory to the three scenarios of item, tag and sequential-set recommendation. Furthermore extensions of time-variant factors and one-class problems are studied. This book generalizes and builds on work that has received the `WWW 2010 Best Paper Award', the `WSDM 2010 Best Student Paper Award' and the `ECML/PKDD 2009 Best Discovery Challenge Award'.

Keywords

Computational Intelligence Context-aware Ranking Factorization Models Recommender Systems

Authors and affiliations

  • Steffen Rendle
    • 1
  1. 1.Wirtschaftsinformatik und Maschinelles LernenUniversität HildesheimHildesheimGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-16898-7
  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-16897-0
  • Online ISBN 978-3-642-16898-7
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site