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Recommender Systems for Social Tagging Systems (Paperback, 2012)
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Recommender Systems for Social Tagging Systems (Paperback, 2012)
Series: SpringerBriefs in Electrical and Computer Engineering
Expected to ship within 10 - 15 working days
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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.
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