Abstract
This work proposes an approach to tag recommendation based on a logistic regression based system. The goal of the method is to support users of current social network systems by providing a rank of new meaningful tags for a resource. This system provides a ranked tag set and it feeds on different posts depending on the resource for which the user requests the recommendation. The performance of this approach is tested according to several evaluation measures, one of them proposed in this paper (\(F_1^+\)). The experiments show that this learning system outperforms certain benchmark recommenders.
This work was supported by the Spanish Ministerio de Educación y Ciencia and the European Regional Development Fund [TIN2007-61273].
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Quevedo, J.R., Montañés, E., Ranilla, J., Díaz, I. (2010). Ranked Tag Recommendation Systems Based on Logistic Regression . In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_29
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DOI: https://doi.org/10.1007/978-3-642-13769-3_29
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