Tag Ranking by Linear Relational Neighborhood Propagation
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.