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Formal Models for Expert Finding on DBLP Bibliography Data

10 years 4 months ago
Formal Models for Expert Finding on DBLP Bibliography Data
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 nea...
Hongbo Deng, Irwin King, Michael R. Lyu
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICDM
Authors Hongbo Deng, Irwin King, Michael R. Lyu
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