Towards the Semantic Web: Collaborative Tag Suggestions

By Zhichen Xu, Yun Fu, Jianchang Mao, Difu Su. 2006

445 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ecfcbf473a22f9a25e3eeb44db5a834950168560

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Recommender Systems (2012, methodology): “Due to the success of collaborative filtering, many works were devoted to using tags to measure similarity among users or objects, and then fuse with the standard memory-based collaborative filtering framework [280–282].”
Tag recommendations based on tensor dimensionality reduction (2008, methodology): “We use Folkrank [8] and the Collaborative Tag Suggestions [18] algorithm (known as Penalty-Reward algorithm), denoted as FolkRank and PR, respectively as comparisons to our approach.”
Social Network Data Analytics (2011, methodology): “To this mix of graph clustering and community discovery algorithms one can also include Markov Clustering (MCL), a graph clustering algorithm based on (stochastic) ow simulation [115].”
Evaluating similarity measures for emergent semantics of social tagging (2009, unknown): “[48, 24]), and combinations of the two [21].”
Personalized recommendation in social tagging systems using hierarchical clustering (2008, background): “In [19], a group of tags are offered to the user based on several criteria (coverage, popularity, effort, uniformity) resulting in a cluster of a relevant tags.”
Latent dirichlet allocation for tag recommendation (2009, background): “Such generic tags are not necessarily useful for ﬁnding speciﬁc resources.”
Human-competitive tagging using automatic keyphrase extraction (2009, background): “For example, Xu et al. (2006) define an authority metric that assigns high scores to those users who match other users’ choices on the same documents, in order to eliminate spammers.”