Learning recommendations in social media systems by weighting multiple relations
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Local metric learning for tag recommendation in social networks
Proceedings of the 11th ACM symposium on Document engineering
Learning to tag text from rules and examples
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Classification and annotation in social corpora using multiple relations
Proceedings of the 20th ACM international conference on Information and knowledge management
Tag Ranking by Linear Relational Neighbourhood Propagation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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We consider here the task of multi-label classification for data organized in a multi-relational graph. We propose the IMMCA model - Iterative Multi-label Multi-Relational Classification Algorithm - a general algorithm for solving the inference and learning problems for this task. Inference is performed iteratively by propagating scores according to the multi-relational structure of the data. We detail two instances of this general model, implementing two different label propagation schemes on the multi-graph. This is the first collective classification method able to handle multiple relations and to perform multi-label classification in multi-graphs. The target application is image annotation in large social media sharing web sites (Flickr). The goal is to assign labels for images when users and images are connected through multiple relations - authorship, friendship, or visual/textual similarities. We show that our model is able to deal with both content and social relations and performs well on real datasets. Additional experiments on artificial data allow us analyzing the behavior of our method in different situations.