{"id":323780,"date":"2019-01-17T09:51:32","date_gmt":"2019-01-17T17:51:32","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=323780"},"modified":"2019-01-17T09:51:32","modified_gmt":"2019-01-17T17:51:32","slug":"goals-activities-predictions-location-geotagged-queries","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/goals-activities-predictions-location-geotagged-queries\/","title":{"rendered":"Here and There: Goals, Activities, and Predictions about Location from Geotagged Queries"},"content":{"rendered":"<p>A signi\ufb01cant portion of Web search is performed in mobile settings. We explore the links between users\u2019 queries on mobile devices and their locations and movement, with a focus on interpreting queries about addresses. We \ufb01nd that users tend to have a primary location, likely corresponding to home or workplace, and that a user\u2019s location relative to this primary location systematically in\ufb02uences the patterns of address searches. We apply our \ufb01ndings to construct a statistical model that can predict with high accuracy whether a user will be soon observed at an address that had been recently retrieved via search. Such an ability to predict that a user will transition to a location can be harnessed for multiple uses including provision of directions and traf\ufb01c information, the rendering of competitive advertising, and guiding the opportunistic completion of pending tasks that can be accomplished en route to a target location.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A signi\ufb01cant portion of Web search is performed in mobile settings. We explore the links between users\u2019 queries on mobile devices and their locations and movement, with a focus on interpreting queries about addresses. We \ufb01nd that users tend to have a primary location, likely corresponding to home or workplace, and that a user\u2019s location [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"SGIR'13","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro\u00ef\u00ac\u0081t or commercial advantage and that copies bear this notice and the full citation on the \u00ef\u00ac\u0081rst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci\u00ef\u00ac\u0081c permission and\/or a fee. Request permissions from permissions@acm.org. SIGIR'13, July 28-August 1, 2013, Dublin, Ireland. Copyright 2013 ACM 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