While existing user modeling solutions are devoted to inferring user attribute independently, in this chapter, we investigate the problem of relational user attribute inference. The task of attribute relation mining and user attribute inference are addressed in a unified framework.
Given results from multimedia content analysis and user modeling, personalized multimedia services are developed to satisfy customized needs. In this chapter, we introduce a general solution framework for personalized multimedia search.
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We first propose a multimodal generative model to simultaneously address tasks of multimedia content analysis and user modeling, and then present the risk minimization-based theoretical framework for personalized image search. The framework considers the noisy tag issue and enables easy incorporation of social relation.
Social multimedia contributes significantly to the arrival of the Big Data era. In this chapter, basic tasks of user-centric social multimedia computing are extended under the cross-network circumstances, by exploiting the overlapped users among social media networks. The past decade has witnessed the rapid popularity of multimedia generation and consuming via social media, which features in diversity, heterogeneity, and interconnection.
These unique characteristics have posed challenges to social multimedia computing and applications. In this book, we have introduced our research on social multimedia computing from the user-centric perspective.
IEEE International Conference on Multimedia & Expo (ICME )
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The published papers are expected to present results of significant value to solve the various problems with application services and other problems which are within the scope of HCIS. In addition, we expect they will trigger further related research and technological improvements relevant to our future lives.
Alejandro (Alex) Jaimes, Ph.D.
User-centric Social Multimedia Computing pp Cite as. Different from traditional and web multimedia computing which are content-centric, social multimedia computing is essentially user-centric: 1 social multimedia data is constituted by what users see, listen, think, feel, and speak; 2 social multimedia analysis and application is toward customized user services.
In this chapter, we first give an overview of social multimedia computing, introduce the challenges and progresses in this field, and then describe the specifications of user-centric social multimedia computing. At the end, we outline the structure of this book.
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Baluja, S. In: WWW, , pp. Bender, M. Boll, S.https://kessai-payment.com/hukusyuu/logiciels-espion/gulex-suivre-un-tel.php
Cai, Y. In: CIKM, , pp. Cao, L. ACM Google Scholar.