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A probabilistic model of geographic relevance


De Sabbata, S; Reichenbacher, T (2010). A probabilistic model of geographic relevance. In: Association for Computing Machinery. GIR'10: Workshop on Geographical Information Retrieval. New York: ACM , 1- 2.

Abstract

In this paper, we present a new model for the assessment of Geographic Relevance. This model is drawn from Okapi BM25, thus it takes into account not only a score for each di- mension of relevance but also the distribution of these scores within the collection. Preliminary results suggest that the relevance estimation of top-ranked objects is more sensitive to small changes in the user context.

Abstract

In this paper, we present a new model for the assessment of Geographic Relevance. This model is drawn from Okapi BM25, thus it takes into account not only a score for each di- mension of relevance but also the distribution of these scores within the collection. Preliminary results suggest that the relevance estimation of top-ranked objects is more sensitive to small changes in the user context.

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Additional indexing

Item Type:Book Section, refereed, further contribution
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2010
Deposited On:02 Feb 2011 09:35
Last Modified:05 Apr 2016 14:32
Publisher:ACM
ISBN:978-1-60558-826-1
Funders:University of Zurich
Additional Information:6th Workshop on Geographic Information Retrieval, GIR'10, Zurich, Switzerland — January 28 - 29, 2010
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1145/1722080.1722109

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