Collaborative Tag Recommendation System based on Logistic Regression

This work proposes an approach to collaborative tag recommendation based on a machine learning system for probabilistic regression. 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 recommendation is requested and on the user who requests the recommendation. Di_erent kinds of collaboration among users and resources are introduced. That collaboration adds to the training set additional posts carefully selected according to the interaction among users and/or resources. Furthermore, a selection of post using scoring measures is also proposed including a penalization of oldest post. The performance of these approaches is tested according to F1 but just considering at most the first five tags of the ranking, which is the evaluation measure proposed in ECML PKDD Discovery Challenge 2009. The experiments were carried out over two different kind of data sets of Bibsonomy folksonomy, core and no core, reaching a performance of 26:25% for the former and 6:98% for the latter.