Books > Computing & IT > Computer communications & networking
|
Buy Now
Learning from Multiple Social Networks (Paperback)
Loot Price: R1,079
Discovery Miles 10 790
|
|
Learning from Multiple Social Networks (Paperback)
Series: Synthesis Lectures on Information Concepts, Retrieval, and Services
Expected to ship within 10 - 15 working days
|
With the proliferation of social network services, more and more
social users, such as individuals and organizations, are
simultaneously involved in multiple social networks for various
purposes. In fact, multiple social networks characterize the same
social users from different perspectives, and their contexts are
usually consistent or complementary rather than independent. Hence,
as compared to using information from a single social network,
appropriate aggregation of multiple social networks offers us a
better way to comprehensively understand the given social users.
Learning across multiple social networks brings opportunities to
new services and applications as well as new insights on user
online behaviors, yet it raises tough challenges: (1) How can we
map different social network accounts to the same social users? (2)
How can we complete the item-wise and block-wise missing data? (3)
How can we leverage the relatedness among sources to strengthen the
learning performance? And (4) How can we jointly model the
dual-heterogeneities: multiple tasks exist for the given
application and each task has various features from multiple
sources? These questions have been largely unexplored to date. We
noticed this timely opportunity, and in this book we present some
state-of-the-art theories and novel practical applications on
aggregation of multiple social networks. In particular, we first
introduce multi-source dataset construction. We then introduce how
to effectively and efficiently complete the item-wise and
block-wise missing data, which are caused by the inactive social
users in some social networks. We next detail the proposed
multi-source mono-task learning model and its application in
volunteerism tendency prediction. As a counterpart, we also present
a mono-source multi-task learning model and apply it to user
interest inference. We seamlessly unify these models with the
so-called multi-source multi-task learning, and demonstrate several
application scenarios, such as occupation prediction. Finally, we
conclude the book and figure out the future research directions in
multiple social network learning, including the privacy issues and
source complementarity modeling. This is preliminary research on
learning from multiple social networks, and we hope it can inspire
more active researchers to work on this exciting area. If we have
seen further it is by standing on the shoulders of giants.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.