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Tag Ranking by Linear Relational Neighborhood Propagation

Boris Chidlovskii
We propose a new method for tag recommendation for content objects. We propose a tag recommendation method which can assist users in tagging process by suggesting relevant tags or directly expand the set of tags. The method is based on query-based ranking on relational multi-type graphs which capture the annotation relationship between objects and tags, as well as the object similarity and tag correlation. The additional advance consists in extending the linear neighbourhood propagation to the relational graphs with the Laplacian regularization framework. Experiments on a large-scale tagging data set collected from Flickr have demonstrated that our proposed algorithm significant.
The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM 2012 - Kadir Has University, Istanbul, Turkey, 26-29 August, 2012.