Automatic ranking of information retrieval systems using data fusion

@article{NurayTuran2006AutomaticRO,
  title={Automatic ranking of information retrieval systems using data fusion},
  author={Rabia Nuray-Turan and Fazli Can},
  journal={Inf. Process. Manage.},
  year={2006},
  volume={42},
  pages={595-614}
}
Measuring effectiveness of information retrieval (IR) systems is essential for research and development and for monitoring search quality in dynamic environments. In this study, we employ new methods for automatic ranking of retrieval systems. In these methods, we merge the retrieval results of multiple systems using various data fusion algorithms, use the top-ranked documents in the merged result as the ‘‘(pseudo) relevant documents,’’ and employ these documents to evaluate and rank the… CONTINUE READING
Highly Influential
This paper has highly influenced 16 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 145 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 87 extracted citations

145 Citations

01020'08'11'14'17
Citations per Year
Semantic Scholar estimates that this publication has 145 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 24 references

Collective choice and social welfare. Amsterdam, The Netherlands: Elsevier

  • A K.
  • World Wide Web conference (pp
  • 1979
Highly Influential
8 Excerpts

Condorcet fusion for improved retrieval

  • A. Kawaguchi
  • Proceedings of the 11 th international conference…
  • 2002
Highly Influential
4 Excerpts

The effects of fitness functions on generic programming-based ranking discovery

  • E. A. Fox, P. Pathak, H. Wu
  • World Wide Web conference (pp
  • 2004

Similar Papers

Loading similar papers…