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How Relevant is the Irrelevant Data
2016
Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM '16
For the task of tag-based item recommendations, the underlying tensor model faces several challenges such as high data sparsity and inferring latent factors effectively. To overcome the inherent sparsity issue of tensor models, we propose the graded-relevance interpretation scheme that leverages the tagging data effectively. Unlike the existing schemes, the graded-relevance scheme interprets the tagging data richly, differentiates the non-observed tagging data insightfully, and annotates each
doi:10.1145/2835776.2835790
dblp:conf/wsdm/IfadaN16
fatcat:vegnwhmilnggpl5fifhb4g6h4e