Sparse Spatial Selection for Novelty-Based Search Result Diversification [chapter]

Veronica Gil-Costa, Rodrygo L. T. Santos, Craig Macdonald, Iadh Ounis
2011 Lecture Notes in Computer Science  
Novelty-based diversification approaches aim to produce a diverse ranking by directly comparing the retrieved documents. However, since such approaches are typically greedy, they require O(n 2 ) documentdocument comparisons in order to diversify a ranking of n documents. In this work, we propose to model novelty-based diversification as a similarity search in a sparse metric space. In particular, we exploit the triangle inequality property of metric spaces in order to drastically reduce the
more » ... er of required document-document comparisons. Thorough experiments using three TREC test collections show that our approach is at least as effective as existing novelty-based diversification approaches, while improving their efficiency by an order of magnitude.
doi:10.1007/978-3-642-24583-1_34 fatcat:annyxwdb6rgsbfno4ov2jf6nne