  Recent developments in the field of data fusion have seen a focus on
techniques that use training queries to estimate the probability that various
documents are relevant to a given query and use that information to assign
scores to those documents on which they are subsequently ranked. This paper
introduces SlideFuse, which builds on these techniques, introducing a sliding
window in order to compensate for situations where little relevance information
is available to aid in the estimation of probabilities.
  SlideFuse is shown to perform favourably in comparison with CombMNZ, ProbFuse
and SegFuse. CombMNZ is the standard baseline technique against which data
fusion algorithms are compared whereas ProbFuse and SegFuse represent the
state-of-the-art for probabilistic data fusion methods.
