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Recommender Systems for the Social Web

✍ Scribed by José J. Pazos Arias, Ana FernÑndez Vilas, Rebeca P. Díaz Redondo (auth.)


Publisher
Springer-Verlag Berlin Heidelberg
Year
2012
Tongue
English
Leaves
243
Series
Intelligent Systems Reference Library 32
Edition
1
Category
Library

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✦ Synopsis


The recommendation of products, content and services cannot be considered newly born, although its widespread application is still in full swing. While its growing success in numerous sectors, the progress of the Social Web has revolutionized the architecture of participation and relationship in the Web, making it necessary to restate recommendation and reconciling it with Collaborative Tagging, as the popularization of authoring in the Web, and Social Networking, as the translation of personal relationships to the Web. Precisely, the convergence of recommendation with the above Social Web pillars is what motivates this book, which has collected contributions from well-known experts in the academy and the industry to provide a broader view of the problems that Social Recommendersmight face with. If recommender systems have proven their key role in facilitating the user access to resources on the Web, when sharing resources has become social, it is natural for recommendation strategies in the Social Web era take into account the users’ point of view and the relationships among users to calculate their predictions. This book aims to help readers to discover and understand the interplay among legal issues such as privacy; technical aspects such as interoperability and scalability; and social aspects such as the influence of affinity, trust, reputation and likeness, when the goal is to offer recommendations that are truly useful to both the user and the provider.

✦ Table of Contents


Front Matter....Pages 1-17
Front Matter....Pages 1-1
Social Recommender Systems....Pages 3-42
Legal Aspects of Recommender Systems in the Web 2.0: Trust, Liability and Social Networking....Pages 43-62
Front Matter....Pages 63-63
Challenges in Tag Recommendations for Collaborative Tagging Systems....Pages 65-87
A Multi-criteria Approach for Automatic Ontology Recommendation Using Collective Knowledge....Pages 89-103
Front Matter....Pages 105-105
Implicit Trust Networks: A Semantic Approach to Improve Collaborative Recommendations....Pages 107-119
Social Recommendation Based on a Rich Aggregation Model....Pages 121-135
Front Matter....Pages 137-137
Group Recommender Systems: New Perspectives in the Social Web....Pages 139-157
Augmenting Collaborative Recommenders by Fusing Social Relationships: Membership and Friendship....Pages 159-175
Front Matter....Pages 177-177
Recommendations on the Move....Pages 179-193
SCORM and Social Recommendation: A Web 2.0 Approach to E-learning....Pages 195-207
Front Matter....Pages 209-209
Conclusiones and Open Trends....Pages 211-222

✦ Subjects


Computational Intelligence; Artificial Intelligence (incl. Robotics)


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