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Topic modeling has emerged as a popular learning technique not only in mining text representations, but also in modeling authors' interests and influence, as well as predicting linkage among documents or authors. However, few existing topic models distinguish and make use of the prior knowledge in regard to the different importance of documents (authors) over topics. In this paper, we focus on the ability of topic models in modeling author interests and influence. We introduce a pair-wise baseddoi:10.24507/ijicic.11.04.1295 fatcat:2i27jfu34batbdjdgwfrafv42m