Contribution

An Axiomatic Approach for Result Diversification

Resource type
Authors/contributors
Title
An Axiomatic Approach for Result Diversification
Abstract
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.
Date
2009
Proceedings Title
Proceedings of the 18th International Conference on World Wide Web
Place
New York, NY, USA
Publisher
ACM
Pages
381–390
Series
WWW '09
Language
en
DOI
10.1145/1526709.1526761
ISBN
978-1-60558-487-4
Accessed
2019-01-27T22:06:28Z
Library Catalog
ACM Digital Library
Citation
Gollapudi, S., & Sharma, A. (2009). An Axiomatic Approach for Result Diversification. In Proceedings of the 18th International Conference on World Wide Web (pp. 381–390). New York, NY, USA: ACM. https://doi.org/10.1145/1526709.1526761
Field of study