document: An axiomatic approach for result diversification Understanding user intent is key to designing an effective ranking system in a search engine . In the absence of any explicit knowledge of user intent , search engines want to diversify results to improve user satisfaction . In such a setting , the probability ranking principle-based approach of presenting the most relevant results on top can be sub-optimal , and hence the search engine would like to trade-off relevance for diversity in the results . In analogy to prior work on ranking and clustering systems , we use the axiomatic approach to characterize and design diversification systems . We develop a set of natural axioms that a diversification system is expected to satisfy , and show that no diversification function can satisfy all the axioms simultaneously . We illustrate the use of the axiomatic framework by providing three example diversification objectives that satisfy different subsets of the axioms . We also uncover a rich link to the facility dispersion problem that results in algorithms for a number of diversification objectives . Finally , we propose an evaluation methodology to characterize the objectives and the underlying axioms . We conduct a large scale evaluation of our objectives based on two data sets : a data set derived from the Wikipedia disambiguation pages and a product database .
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