Paper Info
Title
Tag Ranking by Linear Relational Neighbourhood Propagation
Abstract
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. We report evaluation results on a large-scale Flickr data set.
Year
DOI
Venue
2012
10.1109/ASONAM.2012.40
Advances in Social Networks Analysis and Mining
Keywords
Field
DocType
tag correlation,relevant tag,evaluation result,tag recommendation method,tag ranking,additional advance,linear relational neighbourhood propagation,relational multi-type graph,large-scale flickr data,annotation relationship,laplacian regularization framework,relational graph,graph theory,recommender systems
Graph theory,Recommender system,Graph,Data mining,Annotation,Ranking,Computer science,Correlation,Neighbourhood (mathematics),Artificial intelligence,Laplacian regularization,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4673-2497-7
0
0.34
References 
Authors
6
1
Name
Order
Citations
PageRank
Boris Chidlovskii141152.58