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  • 1
    Online Resource
    Online Resource
    Learning from multiple social networks (2016)
    [San Rafael] : Morgan & Claypool
    Details
    UID:
    gbv_876869800
    Format: 1 Online-Ressource (1 PDF (xv, 102 Seiten)) , Illustrationen
    Edition: Also available in print
    ISBN: 1627059865 , 9781627059862
    Series Statement: Synthesis lectures on information concepts, retrieval, and services #48
    Content: 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 dualheterogeneities: 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
    Content: 1. Introduction -- 1.1 Background -- 1.2 Motivation -- 1.3 Challenges -- 1.4 Our solutions and applications -- 1.5 Outline of this book --
    Content: 2. Data gathering and completion -- 2.1 User accounts alignment -- 2.2 Missing data problems -- 2.3 Matrix factorization for data completion -- 2.4 Multiple social networks data completion -- 2.5 Summary --
    Content: 3. Multi-source mono-task learning -- 3.1 Application: volunteerism tendency prediction -- 3.2 Related work -- 3.2.1 Volunteerism and personality analysis -- 3.2.2 Multi-view learning with missing data -- 3.3 Multiple social network learning -- 3.3.1 Notation -- 3.3.2 Problem formulations -- 3.3.3 Optimization -- 3.4 Experimentation -- 3.4.1 Experimental settings -- 3.4.2 Feature extraction -- 3.4.3 Model comparison -- 3.4.4 Data completion comparison -- 3.4.5 Feature comparison -- 3.4.6 Source comparison -- 3.4.7 Size varying of positive samples -- 3.4.8 Complexity discussion -- 3.5 Summary --
    Content: 4. Mono-source multi-task learning -- 4.1 Application: user interest inference from mono-source -- 4.2 Related work -- 4.2.1 Clustered multi-task learning -- 4.2.2 User interest mining -- 4.3 Efficient clustered multi-task learning -- 4.3.1 Notation -- 4.3.2 Problem formulation -- 4.3.3 Grouping structure learning -- 4.3.4 Efficient clustered multi-task learning -- 4.4 Experimentation -- 4.4.1 Experimental settings -- 4.4.2 Feature extraction -- 4.4.3 Evaluation metric -- 4.4.4 Parameter tuning -- 4.4.5 Model comparison -- 4.4.6 Necessity of structure learning -- 4.5 Summary --
    Content: 5. Multi-source multi-task learning -- 5.1 Application: user interest inference from multi-source -- 5.2 Related work -- 5.3 Multi-source multi-task learning -- 5.3.1 Notation -- 5.3.2 Problem formulations -- 5.3.3 Optimization -- 5.3.4 Construction of interest tree structure -- 5.4 Experiments -- 5.4.1 Experimental settings -- 5.4.2 Model comparison -- 5.4.3 Source comparison -- 5.4.4 Complexity discussion -- 5.5 Summary --
    Content: 6. Multi-source multi-task learning with feature selection -- 6.1 Application: user attribute learning from multimedia data -- 6.2 Related work -- 6.3 Data construction -- 6.3.1 Data crawling strategy -- 6.3.2 Ground truth construction -- 6.4 Multi-source multi-task learning with Fused Lasso -- 6.5 Optimization -- 6.6 Experiments -- 6.6.1 Experimental settings -- 6.6.2 Feature extraction -- 6.6.3 Overall model evaluation -- 6.6.4 Component-wise analysis -- 6.6.5 Source integration -- 6.6.6 Parameter tuning -- 6.6.7 Computational analysis -- 6.7 Other application -- 6.8 Summary --
    Content: 7. Research frontiers -- Bibliography -- Authors' biographies
    Note: Abstract freely available; full-text restricted to subscribers or individual document purchasers , Includes bibliographical references (pages 87-100) , Part of: Synthesis digital library of engineering and computer science , Also available in print. , System requirements: Adobe Acrobat Reader. , Mode of access: World Wide Web.
    Additional Edition: ISBN 1627054243
    Additional Edition: ISBN 9781627054249
    Additional Edition: Print version ISBN 9781627054249
    Language: English
    Keywords: Electronic books
    URL: Volltext
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  • 2
    Online Resource
    Online Resource
    Learning from multiple social networks (2016)
    San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool
    Details
    UID:
    gbv_1654379778
    Format: 1 Online-Ressource (xv, 102 pages) , illustrations.
    ISBN: 9781627059862
    Series Statement: Synthesis lectures on information concepts, retrieval, and services # 48
    Content: 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 dualheterogeneities: 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.
    Note: Part of: Synthesis digital library of engineering and computer science. - Includes bibliographical references (pages 87-100). - Compendex. INSPEC. Google scholar. Google book search. - Title from PDF title page (viewed on May 13, 2016) , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader.
    Additional Edition: ISBN 9781627054249
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781627054249
    Language: English
    DOI: 10.2200/S00714ED1V01Y201603ICR048
    URL: Volltext
    URL: Volltext
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  • 3
    Book
    Book
    Multimodal learning toward micro-video understanding / (2019)
    [San Rafael, California] :Morgan & Claypool Publishers,
    Details
    UID:
    almafu_BV046433364
    Format: xv, 170 Seiten : , Illustrations, Diagramme, Portraits.
    ISBN: 978-1-68173-630-3 , 978-1-68173-628-0
    Series Statement: Synthesis lectures on image, video, and multimedia processing # 20
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-168173-629-7
    Language: English
    Keywords: Social Media ; Multimodales System ; Videoaufzeichnung
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  • 4
    Online Resource
    Online Resource
    Graph Learning for Fashion Compatibility Modeling (2022)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    UID:
    gbv_1832787567
    Format: 1 Online-Ressource(XIV, 112 p. 29 illus., 28 illus. in color.)
    Edition: 2nd ed. 2022.
    ISBN: 9783031188176
    Series Statement: Synthesis Lectures on Information Concepts, Retrieval, and Services
    Content: Introduction -- Correlation-oriented Graph Learning for OCM -- Modality-oriented Graph Learning for OCM -- Unsupervised Disentangled Graph Learning for OCM -- Supervised Disentangled Graph Learning for OCM -- Heterogeneous Graph Learning for Personalized OCM -- Research Frontiers.
    Content: This book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios. In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling. Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years. Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items. This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets. This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items. To deal with the challenging task of outfit compatibility modeling, this book gives comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning. Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers.
    Additional Edition: ISBN 9783031188169
    Additional Edition: ISBN 9783031188183
    Additional Edition: ISBN 9783031188190
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031188169
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031188183
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031188190
    Language: English
    DOI: 10.1007/978-3-031-18817-6
    URL: lizenzpflichtig
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  • 5
    Online Resource
    Online Resource
    Multimodal Learning toward Micro-Video Understanding (2019)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    UID:
    gbv_1823896006
    Format: 1 Online-Ressource(XV, 170 p.)
    Edition: 1st ed. 2019.
    ISBN: 9783031022555
    Series Statement: Synthesis Lectures on Image, Video, and Multimedia Processing
    Content: Preface -- Acknowledgments -- Introduction -- Data Collection -- Multimodal Transductive Learning for Micro-Video Popularity Prediction -- Multimodal Cooperative Learning for Micro-Video Venue Categorization -- Multimodal Transfer Learning in Micro-Video Analysis -- Multimodal Sequential Learning for Micro-Video Recommendation -- Research Frontiers -- Bibliography -- Authors' Biographies.
    Content: 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.
    Additional Edition: ISBN 9783031002168
    Additional Edition: ISBN 9783031011276
    Additional Edition: ISBN 9783031033834
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031002168
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031011276
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031033834
    Language: English
    DOI: 10.1007/978-3-031-02255-5
    URL: lizenzpflichtig
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  • 6
    Online Resource
    Online Resource
    Compatibility modeling : data and knowledge applications for clothing matching (2020)
    [San Rafael, California] : Morgan & Claypool
    Details
    UID:
    gbv_175910275X
    Format: 1 Online-Ressource (1 PDF (xix, 118 pages)) , color illustrations
    Edition: Also available in print
    ISBN: 9781681736693
    Series Statement: Synthesis lectures on information concepts, retrieval, and services #69
    Content: 1. Introduction -- 1.1. Background -- 1.2. Challenges -- 1.3. Our solutions -- 1.4. Book structure
    Content: 2. Data collection -- 2.1. Dataset I for general compatibility modeling -- 2.2. Dataset II for personalized compatibility modeling -- 2.3. Dataset III for personalized wardrobe creation -- 2.4. Summary
    Content: 3. Data-driven compatibility modeling -- 3.1. Introduction -- 3.2. Related work -- 3.3. Methodology -- 3.4. Experiment -- 3.5. Summary
    Content: 4. Knowledge-guided compatibility modeling -- 4.1. Introduction -- 4.2. Related work -- 4.3. Methodology -- 4.4. Experiment -- 4.5. Summary
    Content: 5. Prototype-wise interpretable compatibility modeling -- 5.1. Introduction -- 5.2. Related work -- 5.3. Methodology -- 5.4. Experiment -- 5.5. Summary
    Content: 6. Personalized compatibility modeling -- 6.1. Introduction -- 6.2. Related work -- 6.3. Methodology -- 6.4. Experiment -- 6.5. Summary
    Content: 7. Personalized capsule wardrobe creation -- 7.1. Introduction -- 7.2. Related work -- 7.3. PCW-DC -- 7.4. Body shape assignment scheme -- 7.5. Experiments -- 7.6. Summary
    Content: 8. Research frontiers -- 8.1. Generative compatibility modeling -- 8.2. Virtual try-on with arbitrary pose -- 8.3. Clothing generation.
    Content: Nowadays, fashion has become an essential aspect of people's daily life. As each outfit usually comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper outfit largely relies on the harmonious matching of these items. Nevertheless, not everyone is good at outfit composition, especially those who have a poor fashion aesthetic. Fortunately, in recent years the number of online fashion-oriented communities, like IQON and Chictopia, as well as e-commerce sites, like Amazon and eBay, has grown. The tremendous amount of real-world data regarding people's various fashion behaviors has opened a door to automatic clothing matching. Despite its significant value, compatibility modeling for clothing matching that assesses the compatibility score for a given set of (equal or more than two) fashion items, e.g., a blouse and a skirt, yields tough challenges: (a) the absence of comprehensive benchmark; (b) comprehensive compatibility modeling with the multi-modal feature variables is largely untapped; (c) how to utilize the domain knowledge to guide the machine learning; (d) how to enhance the interpretability of the compatibility modeling; and (e) how to model the user factor in the personalized compatibility modeling. These challenges have been largely unexplored to date. In this book, we shed light on several state-of-the-art theories on compatibility modeling. In particular, to facilitate the research, we first build three large-scale benchmark datasets from different online fashion websites, including IQON and Amazon. We then introduce a general data-driven compatibility modeling scheme based on advanced neural networks. To make use of the abundant fashion domain knowledge, i.e., clothing matching rules, we next present a novel knowledge-guided compatibility modeling framework. Thereafter, to enhance the model interpretability, we put forward a prototype-wise interpretable compatibility modeling approach. Following that, noticing the subjective aesthetics of users, we extend the general compatibility modeling to the personalized version. Moreover, we further study the real-world problem of personalized capsule wardrobe creation, aiming to generate a minimum collection of garments that is both compatible and suitable for the user. Finally, we conclude the book and present future research directions, such as the generative compatibility modeling, virtual try-on with arbitrary poses, and clothing generation
    Note: Part of: Synthesis digital library of engineering and computer science , Includes bibliographical references (pages 103-116) , Compendex , INSPEC , Google scholar , Google book search , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader.
    Additional Edition: ISBN 9781681736686
    Additional Edition: ISBN 9781681736709
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781681736686
    Additional Edition: ISBN 9781681736709
    Language: English
    Keywords: Electronic books
    URL: lizenzpflichtig
    URL: lizenzpflichtig
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  • 7
    Online Resource
    Online Resource
    Proceedings of the 1st Workshop on Multimedia Computing towards Fashion Recommendation (2022)
    New York,NY,United States : Association for Computing Machinery
    Details
    UID:
    gbv_1821304136
    Format: 1 online resource (44 pages)
    ISBN: 9781450394987
    Series Statement: ACM Conferences
    Note: Title from The ACM Digital Library
    Language: English
    Keywords: Konferenzschrift
    DOI: 10.1145/3552468
    URL: lizenzpflichtig
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