{"id":168673,"date":"2015-02-01T00:00:00","date_gmt":"2015-02-01T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/you-are-where-you-go-inferring-demographic-attributes-from-location-check-ins\/"},"modified":"2018-10-16T20:42:23","modified_gmt":"2018-10-17T03:42:23","slug":"you-are-where-you-go-inferring-demographic-attributes-from-location-check-ins","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/you-are-where-you-go-inferring-demographic-attributes-from-location-check-ins\/","title":{"rendered":"You Are Where You Go: Inferring Demographic Attributes from Location Check-ins"},"content":{"rendered":"<div class=\"asset-content\">\n<p>User profiling is crucial to many online services. Several recent studies suggest that demographic attributes are predictable from different online behavioral data, such as users\u2019 \u201cLikes\u201d on Facebook, friendship relations, and the linguistic characteristics of tweets. But location check-ins, as a bridge of users\u2019 offline and online lives, have by and large been overlooked in inferring user profiles.<\/p>\n<p>In this paper, we investigate the predictive power of location check-ins for inferring users\u2019 demographics and propose a simple yet general <em>location to profile (L2P)<\/em> framework. More specifically, we extract rich semantics of users\u2019 check-ins in terms of <em>spatiality<\/em> , <em>temporality<\/em>, and <em>location knowledge<\/em>, where the location knowledge is enriched with semantics mined from heterogeneous domains including both online customer review sites and social networks. Additionally, tensor factorization is employed to draw out low dimensional representations of users\u2019 intrinsic check-in preferences considering the above factors. Meanwhile, the extracted features are used to train predictive models for inferring various demographic attributes.<\/p>\n<p>We collect a large dataset consisting of profiles of 159, 530 verified users from an online social network. Extensive experimental results based upon this dataset validate that: 1) Location checkins are diagnostic representations of a variety of demographic attributes, such as gender, age, education background, and marital status; 2) The proposed framework substantially outperforms compared models for profile inference in terms of various evaluation metrics, such as precision, recall, F-measure, and AUC.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>User profiling is crucial to many online services. Several recent studies suggest that demographic attributes are predictable from different online behavioral data, such as users\u2019 \u201cLikes\u201d on Facebook, friendship relations, and the linguistic characteristics of tweets. But location check-ins, as a bridge of users\u2019 offline and online lives, have by and large been overlooked in [&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":"ACM - Association for Computing Machinery","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","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":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. 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