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(n2) document-document 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 number 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. © 2011 Springer-Verlag.
CITATION STYLE
Gil-Costa, V., Santos, R. L. T., MacDonald, C., & Ounis, I. (2011). Sparse spatial selection for novelty-based search result diversification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7024 LNCS, pp. 344–355). https://doi.org/10.1007/978-3-642-24583-1_34
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