On inferring image label information using rank minimization for supervised concept embedding
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Automatically tagging email by leveraging other users' folders
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Sentiment classification based on supervised latent n-gram analysis
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Personalizing automated image annotation using cross-entropy
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Proceedings of the 21st international conference on World Wide Web
Human wayfinding in information networks
Proceedings of the 21st international conference on World Wide Web
WSABIE: scaling up to large vocabulary image annotation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Proceedings of the sixth ACM conference on Recommender systems
Iterative relevance feedback with adaptive exploration/exploitation trade-off
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Sentiment classification with supervised sequence embedding
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Co-factorization machines: modeling user interests and predicting individual decisions in Twitter
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A picture is worth a thousand tags: automatic web based image tag expansion
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages
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Social collaborative retrieval
Proceedings of the 7th ACM international conference on Web search and data mining
Estimating ad group performance in sponsored search
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Framing image description as a ranking task: data, models and evaluation metrics
Journal of Artificial Intelligence Research
Learning semantic representations of objects and their parts
Machine Learning
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Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at k of the ranked list of annotations for a given image and learning a low-dimensional joint embedding space for both images and annotations. Our method both outperforms several baseline methods and, in comparison to them, is faster and consumes less memory. We also demonstrate how our method learns an interpretable model, where annotations with alternate spellings or even languages are close in the embedding space. Hence, even when our model does not predict the exact annotation given by a human labeler, it often predicts similar annotations, a fact that we try to quantify by measuring the newly introduced "sibling" precision metric, where our method also obtains excellent results.