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  1. International Journal of Multimedia Information Retrieval
  2. International Journal of Multimedia Information Retrieval : Volume 5
  3. International Journal of Multimedia Information Retrieval : Volume 5, Issue 3, September 2016
  4. Learning content–social influential features for influence analysis
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International Journal of Multimedia Information Retrieval : Volume 6
International Journal of Multimedia Information Retrieval : Volume 5
International Journal of Multimedia Information Retrieval : Volume 5, Issue 4, November 2016
International Journal of Multimedia Information Retrieval : Volume 5, Issue 3, September 2016
Top multimedia information retrieval papers
Open and free datasets for multimedia retrieval
Learning content–social influential features for influence analysis
Deep shape-aware descriptor for nonrigid 3D object retrieval
On the coupled use of signal and semantic concepts to bridge the semantic and user intention gaps for visual content retrieval
Boosting local texture descriptors with Log-Gabor filters response for improved image retrieval
Image recommendation based on keyword relevance using absorbing Markov chain and image features
International Journal of Multimedia Information Retrieval : Volume 5, Issue 2, June 2016
International Journal of Multimedia Information Retrieval : Volume 5, Issue 1, March 2016
International Journal of Multimedia Information Retrieval : Volume 4
International Journal of Multimedia Information Retrieval : Volume 3
International Journal of Multimedia Information Retrieval : Volume 2
International Journal of Multimedia Information Retrieval : Volume 1

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Learning content–social influential features for influence analysis

Content Provider Springer Nature Link
Author Hong, Richang Zhang, Hanwang Zhao, Na Wang, Meng Chua, Tat Seng
Copyright Year 2016
Abstract We address how to measure the information propagation probability between users given certain contents. In sharp contrast to existing works that oversimplify the propagation model as predefined distributions, our approach fundamentally attempts to answer why users are influenced (e.g., by content or relations) and whether the corresponding influential features (e.g., hidden factors) can be inferred from the propagation in the entire network. In particular, we propose a novel method to deeply learn the unified feature representations for both user pair and content, where the homogeneous feature similarity can be used to estimate the propagation probability between users with given content. The features are dubbed content–social influential feature since we consider not only the content of the propagation information but also how it propagates over the social network. We design a fast asynchronous parallel algorithm for the feature learning. Through extensive experiments on a real-world social network with 53 million users and 838 million tweets, we show significantly improved performance as compared to other state-of-the-art methods on various social influence analysis tasks.
Starting Page 137
Ending Page 149
Page Count 13
File Format PDF
ISSN 21926611
Journal International Journal of Multimedia Information Retrieval
Volume Number 5
Issue Number 3
e-ISSN 2192662X
Language English
Publisher Springer London
Publisher Date 2016-06-27
Publisher Place London
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Representation learning Social influence Influence prediction Information Storage and Retrieval Data Mining and Knowledge Discovery Image Processing and Computer Vision Computer Science Social network Information Systems Applications (incl. Internet) Multimedia Information Systems
Content Type Text
Resource Type Article
Subject Library and Information Sciences Information Systems Media Technology
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