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020 _a9783031023002
_9978-3-031-02300-2
024 7 _a10.1007/978-3-031-02300-2
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
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082 0 4 _a004.6
_223
100 1 _aNie, Liqiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978745
245 1 0 _aLearning from Multiple Social Networks
_h[electronic resource] /
_cby Liqiang Nie, Xuemeng Song, Tat-Seng Chua.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXV, 102 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
505 0 _aAcknowledgments -- Introduction -- Data Gathering and Completion -- Multi-source Mono-task Learning -- Mono-source Multi-task Learning -- Multi-source Multi-task Learning -- Multi-source Multi-task Learning with Feature Selection -- Research Frontiers -- Bibliography -- Authors' Biographies .
520 _aWith 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.
650 0 _aComputer networks .
_931572
650 1 4 _aComputer Communication Networks.
_978746
700 1 _aSong, Xuemeng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978747
700 1 _aChua, Tat-Seng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978748
710 2 _aSpringerLink (Online service)
_978749
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031011726
776 0 8 _iPrinted edition:
_z9783031034282
830 0 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
_978750
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02300-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c84648
_d84648