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Context-Aware Ranking with Factorization Models

  • Book
  • © 2011

Overview

Authors:
  1. Steffen Rendle

    1. Wirtschaftsinformatik und Maschinelles Lernen, Universität Hildesheim, Hildesheim, Germany

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  • Presents a unified theory of context-aware ranking that subsumes several recommendation tasks such as item, tag and context-aware recommendation
  • Easily readable and understandable
  • Written by an expert in the field

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

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Table of contents (11 chapters)

  1. Front Matter

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  2. Overview

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Steffen Rendle
      Pages 3-8

    3. Related Work

      • Steffen Rendle
      Pages 9-15

  3. Theory

    1. Front Matter

      Pages 17-17
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    2. Ranking from Incomplete Data

      • Steffen Rendle
      Pages 19-37

    3. Learning Context-Aware Ranking

      • Steffen Rendle
      Pages 39-50

    4. Factorization Models

      • Steffen Rendle
      Pages 51-65

  4. Application

    1. Front Matter

      Pages 67-68
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    2. Item Recommendation

      • Steffen Rendle
      Pages 69-84

    3. Tag Recommendation

      • Steffen Rendle
      Pages 85-111

    4. Sequential-Set Recommendation

      • Steffen Rendle
      Pages 113-133

  5. Extensions

    1. Front Matter

      Pages 135-136
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    2. Time-Variant Factorization Models

      • Steffen Rendle
      Pages 137-153

    3. One-Class Matrix Factorization

      • Steffen Rendle
      Pages 155-170

  6. Conclusion

    1. Front Matter

      Pages 171-171
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    2. Conclusion

      • Steffen Rendle
      Pages 173-176

  7. Back Matter

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Keywords

About this book

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'.

Authors and Affiliations

  • Wirtschaftsinformatik und Maschinelles Lernen, Universität Hildesheim, Hildesheim, Germany

    Steffen Rendle

Bibliographic Information

  • Book Title: Context-Aware Ranking with Factorization Models

  • Authors: Steffen Rendle

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-642-16898-7

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer Berlin Heidelberg 2011

  • Hardcover ISBN: 978-3-642-16897-0Published: 11 November 2010

  • Softcover ISBN: 978-3-642-42397-0Published: 11 October 2014

  • eBook ISBN: 978-3-642-16898-7Published: 18 November 2010

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 180

  • Topics: Computational Intelligence, Artificial Intelligence

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