A Mixture Model for Expert Finding Conference Proceeding uri icon



  • This paper addresses the issue of identifying persons with expertise knowledge on a given topic. Traditional methods usually estimate the relevance between the query and the support documents of candidate experts using, for example, a language model. However, the language model lacks the ability of identifying semantic knowledge, thus results in some right experts cannot be found due to not occurrence of the query terms in the support documents. In this paper, we propose a mixture model based on Probabilistic Latent Semantic Analysis (PLSA) to estimate a hidden semantic theme layer between the terms and the support documents. The hidden themes are used to capture the semantic relevance between the query and the experts. We evaluate our mixture model in a real-world system, ArnetMiner. Experimental results indicate that the proposed model outperforms the language models.

publication date

  • January 1, 2008

Date in CU Experts

  • June 20, 2018 1:00 AM

Full Author List

  • Zhang J; Tang J; Liu L; Li J

Full Editor List

  • Washio T; Suzuki E; Ting KM; Inokuchi A

author count

  • 4

Other Profiles

International Standard Book Number (ISBN) 13

  • 978-3-540-68125-0

Additional Document Info

start page

  • 466

end page

  • 478