| 書目名稱 | Learning from Multiple Social Networks | | 編輯 | Liqiang Nie,Xuemeng Song,Tat-Seng Chua | | 視頻video | http://file.papertrans.cn/583/582943/582943.mp4 | | 叢書名稱 | Synthesis Lectures on Information Concepts, Retrieval, and Services | | 圖書封面 |  | | 描述 | 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 unexplor | | 出版日期 | Book 2016 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-02300-2 | | isbn_softcover | 978-3-031-01172-6 | | isbn_ebook | 978-3-031-02300-2Series ISSN 1947-945X Series E-ISSN 1947-9468 | | issn_series | 1947-945X | | copyright | Springer Nature Switzerland AG 2016 |
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