Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
GPCA: an efficient dimension reduction scheme for image compression and retrieval
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Query-Sensitive Similarity Measure for Content-Based Image Retrieval
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Combining similarity measures in content-based image retrieval
Pattern Recognition Letters
Component based shape retrieval using differential profiles
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Image retrieval using nonlinear manifold embedding
Neurocomputing
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning distance functions for image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multimedia Information Retrieval and Management: Technological Fundamentals and Applications
Multimedia Information Retrieval and Management: Technological Fundamentals and Applications
Kernel principal component analysis for content based image retrieval
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Three things everyone should know to improve object retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Social Media Retrieval
Studying Diffusion of Viral Content at Dyadic Level
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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Despite the fact that firms spend heavily in marketing their brands across social media platforms, very little is understood about what media content, in a predictive manner, can generate high interaction rates among their prospects and customers. However, such understanding can significantly help brand marketers generate desired engagements with their target audience in marketing campaigns. In this paper, we study the problem of predicting a brand's user interactions on social media using the example of Pinterest, an emerging platform that has provided a large volume of brand as well as user data in the form of images. Specifically, we treat the prediction of a brand's user interactions, captured through "repinnings" on Pinterest, as the retrieval of relevant user-pinned images given a brand image. The prototype system that we build incorporates this basic principle, and is tested on a large-scale Pinterest dataset of more than one million images. We demonstrate that our system achieves significant lifts in recalling ground truth repinners of brand images for a variety of brands across several major industry categories.