Social Web Artifacts for Boosting Recommenders

Theory and Implementation

  • Cai-Nicolas Ziegler

Part of the Studies in Computational Intelligence book series (SCI, volume 487)

Table of contents

  1. Front Matter
    Pages 1-16
  2. Laying Foundations

    1. Front Matter
      Pages 1-1
    2. Cai-Nicolas Ziegler
      Pages 3-9
    3. Cai-Nicolas Ziegler
      Pages 11-20
  3. Use of Taxonomic Knowledge

    1. Front Matter
      Pages 21-21
    2. Cai-Nicolas Ziegler
      Pages 23-45
    3. Cai-Nicolas Ziegler
      Pages 47-59
    4. Cai-Nicolas Ziegler
      Pages 61-77
    5. Cai-Nicolas Ziegler
      Pages 79-95
  4. Social Ties and Trust

    1. Front Matter
      Pages 97-97
    2. Cai-Nicolas Ziegler
      Pages 99-131
    3. Cai-Nicolas Ziegler
      Pages 133-151
  5. Amalgamating Taxonomies and Trust

    1. Front Matter
      Pages 153-153
    2. Cai-Nicolas Ziegler
      Pages 155-172
    3. Cai-Nicolas Ziegler
      Pages 173-175
  6. Back Matter
    Pages 177-187

About this book


Recommender systems, software programs that learn from human behavior and make predictions of what products we are expected to appreciate and purchase, have become an integral part of our everyday life. They proliferate across electronic commerce around the globe and exist for virtually all sorts of consumable goods, such as books, movies, music, or clothes.

At the same time, a new evolution on the Web has started to take shape, commonly known as the “Web 2.0” or the “Social Web”: Consumer-generated media has become rife, social networks have emerged and are pulling significant shares of Web traffic. In line with these developments, novel information and knowledge artifacts have become readily available on the Web, created by the collective effort of millions of people.

This textbook presents approaches to exploit the new Social Web fountain of knowledge, zeroing in first and foremost on two of those information artifacts, namely classification taxonomies and trust networks. These two are used to improve the performance of product-focused recommender systems: While classification taxonomies are appropriate means to fight the sparsity problem prevalent in many productive recommender systems, interpersonal trust ties – when used as proxies for interest similarity – are able to mitigate the recommenders' scalability problem.


Collaborative Filtering Collective Intelligence Computational Intelligence Content Discovery Platform Enterprise Bookmarking Personalized Marketing Recommender Platform Recommender Systems Web Recommender Systems

Authors and affiliations

  • Cai-Nicolas Ziegler
    • 1
  1. 1.PAYBACK GmbH (American Express)Albert-Ludwigs-Universität Freiburg i.Br.MünchenGermany

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2013
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-00526-3
  • Online ISBN 978-3-319-00527-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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