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Trust Networks for Recommender Systems

  • Book
  • © 2011

Overview

Authors:
  1. Patricia Victor

    1. , Dept. of Applied Mathematics and Compute, University of Gent, Gent, Belgium

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  2. Chris Cornelis

    1. , Department of Applied Mathematics and, Ghent University, Gent, Belgium

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  3. Martine de Cock

    1. , Dept. of Applied Mathematics and Compute, University of Gent, Gent, Belgium

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  • Includes the first in-depth investigation of the potential of distrust in the newly emerging domain of trust-enhanced recommendation
  • Puts the budding field of trust-enhanced recommender systems more prominently on the map
  • Tackles topical problems in the recommender systems domain such as the identification and recommendations of controversial items, and the mitigation of the cold start problem by offering connection guidance to new users
  • Includes supplementary material: sn.pub/extras

Part of the book series: Atlantis Computational Intelligence Systems (ATLANTISCIS, volume 4)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction

    • Patricia Victor, Chris Cornelis, Martine de Cock
    Pages 1-7

  3. Trust Models

    • Patricia Victor, Chris Cornelis, Martine de Cock
    Pages 9-22

  4. Trust Propagation

    • Patricia Victor, Chris Cornelis, Martine de Cock
    Pages 23-50

  5. Trust Aggregation

    • Patricia Victor, Chris Cornelis, Martine de Cock
    Pages 51-90

  6. Social Recommender Systems

    • Patricia Victor, Chris Cornelis, Martine de Cock
    Pages 91-107

  7. Trust and Distrust-Based Recommendations

    • Patricia Victor, Chris Cornelis, Martine de Cock
    Pages 109-153

  8. Connection Guidance for Cold Start Users

    • Patricia Victor, Chris Cornelis, Martine de Cock
    Pages 155-187

  9. Conclusions

    • Patricia Victor, Chris Cornelis, Martine de Cock
    Pages 189-191

  10. Back Matter

    Pages 193-202
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Keywords

About this book

This book describes research performed in the context of trust/distrust propagation and aggregation, and their use in recommender systems. This is a hot research topic with important implications for various application areas. The main innovative contributions of the work are: -new bilattice-based model for trust and distrust, allowing for ignorance and inconsistency -proposals for various propagation and aggregation operators, including the analysis of mathematical properties -Evaluation of these operators on real data, including a discussion on the data sets and their characteristics. -A novel approach for identifying controversial items in a recommender system -An analysis on the utility of including distrust in recommender systems -Various approaches for trust based recommendations (a.o. base on collaborative filtering), an in depth experimental analysis, and proposal for a hybrid approach -Analysis of various user types in recommender systems to optimize bootstrapping of cold start users.

Authors and Affiliations

  • , Dept. of Applied Mathematics and Compute, University of Gent, Gent, Belgium

    Patricia Victor, Martine de Cock

  • , Department of Applied Mathematics and, Ghent University, Gent, Belgium

    Chris Cornelis

Bibliographic Information

  • Book Title: Trust Networks for Recommender Systems

  • Authors: Patricia Victor, Chris Cornelis, Martine de Cock

  • Series Title: Atlantis Computational Intelligence Systems

  • DOI: https://doi.org/10.2991/978-94-91216-08-4

  • Publisher: Atlantis Press Paris

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Atlantis Press 2011

  • Hardcover ISBN: 978-94-91216-07-7Published: 08 May 2011

  • Softcover ISBN: 978-94-91216-39-8Published: 15 July 2013

  • eBook ISBN: 978-94-91216-08-4Published: 03 May 2011

  • Series ISSN: 1875-7650

  • Series E-ISSN: 2215-1710

  • Edition Number: 1

  • Number of Pages: XIII, 202

  • Topics: Artificial Intelligence, Logic Design

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