Advertisement

Recommender Systems for Social Tagging Systems

  • Leandro Balby Marinho
  • Andreas Hotho
  • Robert Jäschke
  • Alexandros Nanopoulos
  • Steffen Rendle
  • Lars Schmidt-Thieme
  • Gerd Stumme
  • Panagiotis Symeonidis
Book

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Foundations

    1. Front Matter
      Pages 1-1
    2. Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme et al.
      Pages 3-15
    3. Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme et al.
      Pages 17-29
  3. Recommendation Techniques for Social Tagging Systems

    1. Front Matter
      Pages 31-31
    2. Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme et al.
      Pages 33-42
    3. Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme et al.
      Pages 43-74
    4. Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme et al.
      Pages 75-80
  4. Implementing Recommender Systems for Social Tagging

    1. Front Matter
      Pages 81-81
    2. Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme et al.
      Pages 83-95
    3. Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme et al.
      Pages 97-108
    4. Leandro Balby Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme et al.
      Pages 109-111

About this book

Introduction

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

Keywords

Folksonomy Multimode Recommendations Recommender Systems Social Tagging Tag-Aware Recommendations

Authors and affiliations

  • Leandro Balby Marinho
    • 1
  • Andreas Hotho
    • 2
  • Robert Jäschke
    • 3
  • Alexandros Nanopoulos
    • 4
  • Steffen Rendle
    • 5
  • Lars Schmidt-Thieme
    • 6
  • Gerd Stumme
    • 7
  • Panagiotis Symeonidis
    • 8
  1. 1., Systems and Computing DepartmentFederal University of Campina GrandeCampina Grande-PBBrazil
  2. 2., Knowledge & Data Engineering GroupUniversity of KasselKasselGermany
  3. 3., Knowledge & Data Engineering GroupUniversity of KasselKasselGermany
  4. 4., Information SystemsUniversity of HildesheimHildesheimGermany
  5. 5., Social Network AnalysisUniversity of KonstanzKonstanzGermany
  6. 6., Information SystemsUniversity of HildesheimHildesheimGermany
  7. 7., Knowledge & Data Engineering GroupUniversity of KasselKasselGermany
  8. 8., Department of InformaticsAristotle UniversityThessalonikiGreece

Bibliographic information