Optimum polynomial retrieval functions based on the probability ranking principle

@article{Fuhr1989OptimumPR,
  title={Optimum polynomial retrieval functions based on the probability ranking principle},
  author={N. Fuhr},
  journal={ACM Trans. Inf. Syst.},
  year={1989},
  volume={7},
  pages={183-204}
}
  • N. Fuhr
  • Published 1989
  • Computer Science
  • ACM Trans. Inf. Syst.
We show that any approach to developing optimum retrieval functions is based on two kinds of assumptions: first, a certain form of representation for documents and requests, and second, additional simplifying assumptions that predefine the type of the retrieval function. [...] Key Result On the other hand, this approach is not suited to log-linear probabilistic models and it needs large samples of relevance feedback data for its application.Expand
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References

Outline of a General Probabilistic Retrieval Model
  • 64
  • Highly Influential