{"id":160491,"date":"2009-11-03T00:00:00","date_gmt":"2009-11-03T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/mining-correlation-between-locations-using-human-location-history\/"},"modified":"2018-10-16T20:19:35","modified_gmt":"2018-10-17T03:19:35","slug":"mining-correlation-between-locations-using-human-location-history","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mining-correlation-between-locations-using-human-location-history\/","title":{"rendered":"Mining Correlation Between Locations Using Human Location History"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The advance of location-acquisition technologies enables people to record their location histories with spatio-temporal datasets, which imply the correlation between geographical regions. This correlation indicates the relationship between locations in the space of human behavior, and can enable many valuable services, such as sales promotion and location recommendation. In this paper, by taking into account a user\u2019s travel experience and the sequentiality locations have been visited, we propose an approach to mine the correlation between locations from a large number of users\u2019 location histories. We conducted a personalized location recommendation system using the location correlation, and evaluated this system with a large-scale real-world GPS dataset. As a result, our method outperforms the related work using the Pearson correlation.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The advance of location-acquisition technologies enables people to record their location histories with spatio-temporal datasets, which imply the correlation between geographical regions. This correlation indicates the relationship between locations in the space of human behavior, and can enable many valuable services, such as sales promotion and location recommendation. In this paper, by taking into account [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563,13555],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-160491","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of 18th ACM SIGSPATIAL Conference on Advances in Geographical Information 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