Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
Context aware recommendations for user-generated content on a social network site
Proceedings of the seventh european conference on European interactive television conference
Flickr group recommendation based on tensor decomposition
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Exploring Context and Content Links in Social Media: A Latent Space Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graphical Model for Context-Aware Visual Content Recommendation
IEEE Transactions on Multimedia
Modeling Flickr Communities Through Probabilistic Topic-Based Analysis
IEEE Transactions on Multimedia
Recommending Flickr groups with social topic model
Information Retrieval
Image ranking based on user browsing behavior
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Understanding and predicting importance in images
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Towards indexing representative images on the web
Proceedings of the 20th ACM international conference on Multimedia
Towards Annotating Media Contents through Social Diffusion Analysis
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Towards Automatic Image Understanding and Mining via Social Curation
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Exploiting socially-generated side information in dimensionality reduction
Proceedings of the 2nd international workshop on Socially-aware multimedia
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This paper proposes a novel method of discovering a set of image contents sharing a specific context (attributes or implicit meaning) with the help of image collections obtained from social curation platforms. Socially curated contents are promising to analyze various kinds of multimedia information, since they are manually filtered and organized based on specific individual preferences, interests or perspectives. Our proposed method fully exploits the process of social curation: (1) How image contents are manually grouped together by users, and (2) how image contents are distributed in the platform. Our method reveals the fact that image contents with a specific context are naturally grouped together and every image content includes really various contexts that cannot necessarily be verbalized by texts.% A preliminary experiment with a small collection of a million of images yields a promising result.