The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
An algorithmic approach to social networks
An algorithmic approach to social networks
Tag recommendations in social bookmarking systems
AI Communications
A content-based method to enhance tag recommendation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using Tag Co-occurrence for Recommendation
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Link prediction for annotation graphs using graph summarization
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
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One of the primary goals of tag recommendation approaches is to deal with the problem of ambiguity of tags in a folksonomy by helping users to select the most appropriate tag to annotate a resource. We propose in this work, an original approach for tag recommendation applying a link prediction using supervised machine learning. Given a user (target user) and a resource (target resource) the proposed algorithm computes a list of tags best suited for recommending target user to annotate the target resource. It first searches for users similar to the target user. Then a link prediction approach is applied on a temporal sequence of bipartite graphs coding the history of tagging of retrieved similar users. This results in obtaining one or more lists of tags for the target resource or similar resources. These lists are then merged using a list aggregation method to get a single list of tags for recommendation. The first prototype of this approach is described in this article. Preliminary results of applying the proposed approach to real dataset extracted from the bibliographical folksonomy CiteULike1 show the validity of the approach.