Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
Multi-modality in one-class classification
Proceedings of the 19th international conference on World wide web
Survey on social tagging techniques
ACM SIGKDD Explorations Newsletter
Iterative Annotation of Multi-relational Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Improving tag recommendation using social networks
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
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We address the problem of tag recommendation for media objects, like images, videos, etc in social media sharing systems. We propose a framework that 1) extracts both object features and the social context and 2) uses them to learn recommendation rules. The social context is described by different types of information, such as a user's personal objects, the objects of a user's social contacts, the importance of the user in the social network, etc. Both object features and the social context are first used to guide the k-nearest neighbour method for the tag recommendation. We then enhance the method by the local topology adjustment on how the nearest neighbours are selected. We learn a local transformation of the feature space surrounding a given object which pushes together objects with the same tags and puts apart objects with different tags. We show how to learn the Mahalanobis distance metric on multi-tag objects and adopt it to the tag recommendation problem.