Abstract
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) 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.
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Gil-Costa, V., Santos, R.L.T., Macdonald, C., Ounis, I. (2011). Sparse Spatial Selection for Novelty-Based Search Result Diversification. In: Grossi, R., Sebastiani, F., Silvestri, F. (eds) String Processing and Information Retrieval. SPIRE 2011. Lecture Notes in Computer Science, vol 7024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24583-1_34
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DOI: https://doi.org/10.1007/978-3-642-24583-1_34
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