Social tag prediction

By Paul Heymann, Daniel Ramage, Hector Garcia-Molina. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008

390 citations recorded by the source corpus.

Subfields: Information Systems

View Paper: https://www.semanticscholar.org/paper/6f6ab675fd324522cb0a5d4200687e38b9ef8f20

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In Information Systems

Citations in the corpus

Human-competitive tagging using automatic keyphrase extraction (2009, methodology): “One potential solution to inconsistency in folksonomies is to use suggestion tools that automatically compute tags for new documents (e.g. Mishne, 2006; Sood et al. , 2007; Heymann et al. , 2008).”
Latent dirichlet allocation for tag recommendation (2009, methodology): “Table 1 gives a selection of association rules with high conﬁdence mined from our dataset.”
Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs (2015, background): “For example, social tag prediction [ Heymann et al. , 2008 ] aims to assign tags to social network users without linking tags to knowledge bases.”
Recommender Systems (2016, neutral): “Machine learning methods for tag recommendation algorithms are discussed in [250, 555, 556].”
We know what @you #tag: does the dual role affect hashtag adoption? (2012, methodology): “The predication analysis in our work is in general related to the prediction of user behavior in online communities [33, 18, 17, 23].”
Pairwise interaction tensor factorization for personalized tag recommendation (2010, neutral): “[3, 21]) on non-personalized tag recommenders – i.”
SemRec: A Semantic Enhancement Framework for Tag Based Recommendation (2011, background): “Social tagging data can be viewed as a set of triples (Heymann, Ramage, and Garcia-Molina 2008; Guan et al. 2010).”
Learning similarity metrics for event identification in social media (2010, background): “Recent studies [21, 22] showed that social media document tags are accurate content descriptors, and could be used to train a social tagging prediction system.”
Evaluating similarity measures for emergent semantics of social tagging (2009, neutral): “[48, 24]), and combinations of the two [21].”
Learning optimal ranking with tensor factorization for tag recommendation (2009, methodology): “But here we use ŷmax as the theoretical upper bound for non-personalized recommenders because it creates the best non-personalized top-n list for the test set Stest – every other method for non-personalized tag recommendation like [2, 13, 12] is guaranteed to have a lower (or in the best case the sa”