On perfect document rankings for expert search

ABSTRACT

Expert search systems often employ a document search component to identify on-topic documents, which are then used to identify people likely to have relevant expertise. This work investigates the impact of the retrieval effectiveness of the underlying document search component. It has been previously shown that applying techniques to the underlying document search component that normally improve the effectiveness of a document search engine also have a positive impact on the retrieval effectiveness of the expert search engine. In this work, we experiment with fictitious perfect document rankings, to attempt to identify an upper-bound in expert search system performance. Our surprising results infer that non-relevant documents can bring useful expertise evidence, and that removing these does not lead to an upper-bound in retrieval performance.

REFERENCES

• 1: P. Bailey, N. Craswell, A. P. de Vries, and I. Soboroff. Overview of the TREC-2007 Enterprise Track. In Proceedings of TREC-2007.
• 2: Krisztian Balog , Leif Azzopardi , Maarten de Rijke, Formal models for expert finding in enterprise corpora, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148181]
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• 4: Craig Macdonald , Iadh Ounis, Voting for candidates: adapting data fusion techniques for an expert search task, Proceedings of the 15th ACM international conference on Information and knowledge management, November 06-11, 2006, Arlington, Virginia, USA [doi>10.1145/1183614.1183671]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Experimentation, Performance

Keywords:
document search, expert search