Formal Models for Expert Finding on DBLP Bibliography Data

  • Authors:

  • Hongbo Deng;Irwin King;Michael R. Lyu

  • Affiliations:

  • -;-;-

  • Venue:
  • ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
  • Year:
  • 2008

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Abstract

Finding relevant experts in a specific field is often crucial for consulting, both in industry and in academia. The aim of this paper is to address the expert-finding task in a real world academic field. We present three models for expert finding based on the large-scale DBLP bibliography and Google Scholar for data supplementation. The first, a novel weighted language model, models an expert candidate based on the relevance and importance of associated documents by introducing a document prior probability, and achieves much better results than the basic language model. The second, a topic-based model, represents each candidate as a weighted sum of multiple topics, whilst the third, a hybrid model, combines the language model and the topic-based model. We evaluate our system using a benchmark dataset based on human relevance judgments of how well the expertise of proposed experts matches a query topic. Evaluation results show that our hybrid model outperforms other models in nearly all metrics.