A mixture model for expert finding

  • Authors:

  • Jing Zhang;Jie Tang;Liu Liu;Juanzi Li

  • Affiliations:

  • Department of Computer and Technology, Tsinghua University, Beijing, China;Department of Computer and Technology, Tsinghua University, Beijing, China;Department of Computer and Technology, Tsinghua University, Beijing, China;Department of Computer and Technology, Tsinghua University, Beijing, China

  • Venue:
  • PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
  • Year:
  • 2008

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Abstract

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.