The Opposite of Smoothing: A Language Model Approach to Ranking Query-Specific Document Clusters
Oren Kurland, Eyal Krikon · 2008
Exploiting information induced from (query-specific) clustering of top-retrieved documents has long been proposed as means for improving precision at the very top ranks of the returned results. We present a novel language model approach to ranking query-specific clusters by the presumed percentage of relevant documents that they contain. While most previous cluster ranking approaches focus on the cluster as a whole, our model also exploits information induced from documents associated with the cluster. Our model substantially outperforms previous approaches for identifying clusters containing a high relevant-document percentage. Furthermore, using the model to produce document ranking yields precision-at-top-ranks performance that is consistently better than that of the initial ranking upon which clustering is performed; the performance also favorably compares with that of a state-of-the-art pseudo-feedback retrieval method.