This work proposes an approach to tag recommendation based on a learning system. The goal of this 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. This research studies different approaches depending on both the posts selected to form the training set and the features with which they are represented. The performance of these approaches are tested according to several evaluation measures; one of them is proposed in this paper F1+ which takes into account the positions where the system has ranked the positive tags at the same time that it considers the cases where positive tags could not be ranked. These experiments show that this learning system outperforms certain benchmark recommenders.