Ranked tag recommendation systems based on logistic regression

Authors:
J. R. Quevedo;E. Montañés;J. Ranilla;I. Díaz
Affiliations:
Computer Science Department, University of Oviedo;Computer Science Department, University of Oviedo;Computer Science Department, University of Oviedo;Computer Science Department, University of Oviedo
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

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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.