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and Sharma, Aneesh 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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