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Tutorial on using social trust for recommender systems
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ACM Conference On Recommender Systems archive
Proceedings of the third ACM conference on Recommender systems table of contents
New York, New York, USA
TUTORIAL SESSION: Tutorials table of contents
Pages 425-426  
Year of Publication: 2009
ISBN:978-1-60558-435-5
Author
Jennifer Golbeck  University of Maryland, College Park, College Park, MD, USA
Sponsor
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

As the Web has shifted to an interactive environment where vast amounts of content is created by users, the question of whom to trust and what information to trust has become both more important and more difficult to answer. At the same time, social networks have become very popular with over a billion accounts shared across hundreds of networks. Social trust relationships, derived from social networks, are uniquely suited to speak to the quality of online information; recommender systems are designed to personalize, sort, aggregate, and highlight information. Merging social networks, trust, and recommender systems can improve the accuracy of recommendations and improve the user's experience. In this tutorial, we will cover the use of social trust in recommender systems. Topics including the computation of trust in social networks, integration of trust into recommender systems, and a discussion of when trust offers benefits and the challenges it presents.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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