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Where to Stop Reading a Ranked List? Threshold Optimization using Truncated Score Distributions
 

Summary: Where to Stop Reading a Ranked List?
Threshold Optimization using Truncated Score Distributions
Avi Arampatzis1
Jaap Kamps1
Stephen Robertson2
1
University of Amsterdam, The Netherlands
2
Microsoft Research Cambridge, United Kingdom
{avi,kamps}@science.uva.nl ser@microsoft.com
ABSTRACT
Ranked retrieval has a particular disadvantage in comparison with
traditional Boolean retrieval: there is no clear cut-off point where
to stop consulting results. This is a serious problem in some setups.
We investigate and further develop methods to select the rank cut-
off value which optimizes a given effectiveness measure. Assuming
no other input than a system's output for a query--document scores
and their distribution--the task is essentially a score-distribution-
al threshold optimization problem. The recent trend in modeling
score distributions is to use a normal-exponential mixture: normal

  

Source: Arampatzis, Avi - Department of Electrical and Computer Engineering, Democritus University of Thrace

 

Collections: Computer Technologies and Information Sciences