With the development of web 2.0,users are becoming more and more deeply involved in Internet,not only as readers,but also as authors.Wording preference is a well-known phenomenon that different people probably use different words even when they talk about the same topic.We think this phenomenon has a great impact on modeling texts by different authors,especially on topic modeling.This paper proposes a way to model users preference by Dirichlet process (DP) in a topic model frame.Experiments show that our model outperforms the hierarchical Dirichlet process mixture model (DPMM) on a corpus of social tagging data from del.icio.us.Combination of users preference can not only bring better performance on normal topic modeling task,but also discover the users preference.