Abstract
Background:
We present a probabilistic topic-based model for content similarity called pmra that underlies the
related article search feature in PubMed. Whether or not a document is about a particular topic is computed
from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do
not attempt to estimate relevance—but rather our focus is “relatedness”, the probability that a user would want
to examine a particular document given known interest in another. We also describe a novel technique for
estimating parameters that does not require human relevance judgments; instead, the process is based on the
existence of MeSH
R
in MEDLINE
R
.
Results:
The pmra retrieval model was compared against bm25, a competitive probabilistic model that shares
theoretical similarities. Experiments using the test collection from the TREC 2005 genomics track shows a small
but statistically significant improvement of pmra over bm25 in terms of precision.
Conclusions:
Our experiments suggest that the pmra model provides an effective ranking algorithm for related
article search.
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