  We present a novel approach to pseudo-feedback-based ad hoc retrieval that
uses language models induced from both documents and clusters. First, we treat
the pseudo-feedback documents produced in response to the original query as a
set of pseudo-queries that themselves can serve as input to the retrieval
process. Observing that the documents returned in response to the
pseudo-queries can then act as pseudo-queries for subsequent rounds, we arrive
at a formulation of pseudo-query-based retrieval as an iterative process.
Experiments show that several concrete instantiations of this idea, when
applied in conjunction with techniques designed to heighten precision, yield
performance results rivaling those of a number of previously-proposed
algorithms, including the standard language-modeling approach. The use of
cluster-based language models is a key contributing factor to our algorithms'
success.
