Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/38181
Title: Assessing geographic relevance for mobile search: A computational model and its validation via crowdsourcing
Authors: Reichenbacher, Tumasch
De Sabbata, Stefano
Purves, Ross S.
Fabrikant, Sara I.
First Published: 9-Mar-2016
Publisher: Wiley for Association for Information Science and Technology (ASIS&T)
Citation: Journal of the Association for Information Science and Technology, 2016, DOI: 10.1002/asi.23625
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.
DOI Link: 10.1002/asi.23625
ISSN: 2330-1643
2330-1635
Links: http://onlinelibrary.wiley.com/doi/10.1002/asi.23625/abstract
http://hdl.handle.net/2381/38181
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © Association for Information Science and Technology (ASIS&T), 2016. This version of the article is distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Appears in Collections:Published Articles, Dept. of Geography

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