Tag recommendation based on Bayesian principle

Abstract

Social tagging systems have become increasingly a popular way to organize online heterogeneous resources. Tag recommendation is a key feature of social tagging systems. Many works has been done to solve this hard tag recommendation problem and has got same good results these years. Taking into account the complexity of the tagging actions, there still exist many limitations. In this paper, we propose a probabilistic model to solve this tag recommendation problem. The model is based on Bayesian principle, and it's very robust and efficient. For evaluating our proposed method, we have conducted experiments on a real dataset extracted from BibSonomy, an online social bookmark and publication sharing system. Our performance study shows that our method achieves good performance when compared with classical approaches. © 2010 Springer-Verlag.