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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.
Micro-videos, a new form of user-generated contents, have been
spreading widely across various social platforms, such as Vine,
Kuaishou, and Tik Tok. Different from traditional long videos,
micro-videos are usually recorded by smart mobile devices at any
place within a few seconds. Due to its brevity and low bandwidth
cost, micro-videos are gaining increasing user enthusiasm. The
blossoming of micro-videos opens the door to the possibility of
many promising applications, ranging from network content caching
to online advertising. Thus, it is highly desirable to develop an
effective scheme for the high-order micro-video understanding.
Micro-video understanding is, however, non-trivial due to the
following challenges: (1) how to represent micro-videos that only
convey one or few high-level themes or concepts; (2) how to utilize
the hierarchical structure of the venue categories to guide the
micro-video analysis; (3) how to alleviate the influence of
low-quality caused by complex surrounding environments and the
camera shake; (4) how to model the multimodal sequential data,
{i.e.}, textual, acoustic, visual, and social modalities, to
enhance the micro-video understanding; and (5) how to construct
large-scale benchmark datasets for the analysis? These challenges
have been largely unexplored to date. In this book, we focus on
addressing the challenges presented above by proposing some
state-of-the-art multimodal learning theories. To demonstrate the
effectiveness of these models, we apply them to three practical
tasks of micro-video understanding: popularity prediction, venue
category estimation, and micro-video routing. Particularly, we
first build three large-scale real-world micro-video datasets for
these practical tasks. We then present a multimodal transductive
learning framework for micro-video popularity prediction.
Furthermore, we introduce several multimodal cooperative learning
approaches and a multimodal transfer learning scheme for
micro-video venue category estimation. Meanwhile, we develop a
multimodal sequential learning approach for micro-video
recommendation. Finally, we conclude the book and figure out the
future research directions in multimodal learning toward
micro-video understanding.
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