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Diversifying Search Results

Resource type
Authors/contributors
Title
Diversifying Search Results
Abstract
We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value of diversification. We demonstrate empirically that our algorithm scores higher in these generalized metrics compared to results produced by commercial search engines.
Date
2009
Proceedings Title
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Place
New York, NY, USA
Publisher
ACM
Pages
5–14
Series
WSDM '09
Language
en
DOI
10.1145/1498759.1498766
ISBN
978-1-60558-390-7
Accessed
2019-01-27T21:41:12Z
Library Catalog
ACM Digital Library
Citation
Agrawal, R., Gollapudi, S., Halverson, A., & Ieong, S. (2009). Diversifying Search Results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining (pp. 5–14). New York, NY, USA: ACM. https://doi.org/10.1145/1498759.1498766
Field of study
Type of contribution