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Assessing geographic relevance for mobile search: A computational model and its validation via crowdsourcing


Reichenbacher, Tumasch; De Sabbata, Stefano; Purves, Ross S; Fabrikant, Sara I (2016). Assessing geographic relevance for mobile search: A computational model and its validation via crowdsourcing. Journal of the Association for Information Science and Technology, 67(11):2620-2634.

Abstract

The selection and retrieval of relevant information from the information universe on the web is becoming increasingly important in addressing information overload. It has also been recognized that geography is an important criterion of relevance, leading to the research area of geographic information retrieval. As users increasingly retrieve information in mobile situations, relevance is often related to geographic features in the real world as well as their representation in web documents. We present 2 methods for assessing geographic relevance (GR) of geographic entities in a mobile use context that include the 5 criteria topicality, spatiotemporal
proximity, directionality, cluster, and colocation. To determine the effectiveness and validity of these methods, we evaluate them through a user study conducted on the Amazon Mechanical Turk crowdsourcing platform. An analysis of relevance ranks for geographic entities in 3 scenarios produced by two GR methods, 2
baseline methods, and human judgments collected in the experiment reveal that one of the GR methods produces similar ranks as human assessors.

Abstract

The selection and retrieval of relevant information from the information universe on the web is becoming increasingly important in addressing information overload. It has also been recognized that geography is an important criterion of relevance, leading to the research area of geographic information retrieval. As users increasingly retrieve information in mobile situations, relevance is often related to geographic features in the real world as well as their representation in web documents. We present 2 methods for assessing geographic relevance (GR) of geographic entities in a mobile use context that include the 5 criteria topicality, spatiotemporal
proximity, directionality, cluster, and colocation. To determine the effectiveness and validity of these methods, we evaluate them through a user study conducted on the Amazon Mechanical Turk crowdsourcing platform. An analysis of relevance ranks for geographic entities in 3 scenarios produced by two GR methods, 2
baseline methods, and human judgments collected in the experiment reveal that one of the GR methods produces similar ranks as human assessors.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2016
Deposited On:10 Jan 2017 10:50
Last Modified:02 Feb 2018 10:33
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:2330-1635
Additional Information:This is the peer reviewed version of the following article: Reichenbacher T et al: Journal of the Association for Information Science and Technology, Volume 67, Issue 11, 2016, 2620–2634, which has been published in final form at https://doi.org/10.1002/asi.23625. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms).
OA Status:Green
Publisher DOI:https://doi.org/10.1002/asi.23625

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