Abstract
A useful ability for search engines is to be able to rank objects with novelty and diversity: the top k documents retrieved should cover possible interpretations of a query with some distribution, or should contain a diverse set of subtopics related to the user's information need, or contain nuggets of information with little redundancy. Evaluation measures have been introduced to measure the effectiveness of systems at this task, but these measures have worst-case NP-complete computation time. We use simulation to investigate the implications of this for optimization and evaluation of retrieval systems. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Carterette, B. (2009). An analysis of NP-completeness in novelty and diversity ranking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5766 LNCS, pp. 200–211). https://doi.org/10.1007/978-3-642-04417-5_18
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