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Learning from Multiple Social Networks (Synthesis Lectures on Information Concepts, Retrieval, and Services) - Softcover
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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.
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Dr. Liqiang Nie received a B.E. degree from Xi'an Jiaotong University of China, Xi'an, in 2009, and a Ph.D. degree from National University of Singapore, in 2013. Currently, he is a research fellow at the National University of Singapore. His research interests include social network analysis and media search. Various parts of his work have been published in top forums including ACM SIGIR, ACM MM, IJCAI, TOIS, TIST, and TMM. He served as the guest editor and special session chair for several journals and conferences, respectively.Xuemeng Song is currently a Ph.D. student at the School of Computing, National University of Singapore. She received her degree from the University of Science and Technology of China in 2012. Her research interests are information retrieval and social network analysis. She has published several papers in top venues, such as SIGIR and TOIS. In addition, she has served as a reviewer for many top conferences and journals.Dr. Chua is the KITHCT Chair Professor at the School of Co mputing, National University of Singapore. He was the Acting and Founding Dean of the School during 1998-2000. Dr. Chua's main research interest is in multimedia information retrieval and social media analysis. In particular, his research focuses on the extraction, retrieval and question-answering (QA) of text, video, and live media arising from the Web and social networks. He is the director of a multi-million-dollar joint center (named NExT) between NUS and Tsinghua University in China to develop technologies for live media search. The project will gather, mine, search, and organize user-generated content within the cities of Beijing and Singapore. His group participates regularly in TREC-QA and TRECVID video retrieval evaluations. Dr. Chua is active in the international research community. He has organized and served as program committee member of numerous international conferences in the areas of computer graphics, multimedia, and text processing. He is the conference co-chair of ACM Multimedia 2005, ACM CIVR 2005, and ACM SIGIR 2008. He serves on the editorial boards of: ACMTransactions of Information Systems (ACM), Foundationand Trends in Information Retrieval (NOW), The Visual Computer (Springer Verlag), and Multimedia Tools and Applications (Kluwer). He is a member of the steering committee of ICMR (International Conference on Multimedia Retrieval) and Multimedia Modeling conference series and a member of the international review panel of two large-scale research projects in Europe. Dr. Chua serves as the Chairman of the Board of Examiners for the Certified IT Project Management (""CITPM"") in Singapore. He is the independent Director of two publicly listed companies in Singapore. He holds a Ph.D. from the University of Leeds, UK.
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Taschenbuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch. Artikel-Nr. 9783031011726
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9783031011726
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Taschenbuch. Zustand: Neu. Learning from Multiple Social Networks | Liqiang Nie (u. a.) | Taschenbuch | xv | Englisch | 2016 | Springer International Publishing | EAN 9783031011726 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Artikel-Nr. 121975446
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