Automated personal assistants such as Siri, Cortana, and Google Now provide services to help users accomplish tasks, including tools to set reminders. We study how people specify and use reminders. Our study analyzes a sample of six months of logs of user specified reminders from Cortana (Microsoft\u2019s intelligent personal assistant), the first large-scale analysis of such reminders. We focus our analyses on time-based reminders, the most common type of reminder found in the logs. We perform a data-driven analysis to identify common categories of tasks that give rise to these reminders across a large number of users, and we arrange these tasks into a taxonomy. We identify temporal patterns linked to the type of task, time of creation, and terms in the reminder text. Finally, we show that these patterns generalize by addressing a prediction task. Specifically, we show that a reminder\u2019s creation time is a strong feature in predicting the notification time, and that including the reminder text further improves prediction accuracy. The results have implications for the design of systems aimed at helping people to complete tasks and to plan future activities.<\/p>\n","protected":false},"excerpt":{"rendered":"
Automated personal assistants such as Siri, Cortana, and Google Now provide services to help users accomplish tasks, including tools to set reminders. We study how people specify and use reminders. Our study analyzes a sample of six months of logs of user specified reminders from Cortana (Microsoft\u2019s intelligent personal assistant), the first large-scale analysis of […]<\/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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13545,13555],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-359789","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the 24th ACM Conference on User Modeling, Adaptation, and Personalization (UMAP '16)","msr_affiliation":"","msr_published_date":"2016-07-13","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"Best Student Paper","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"359792","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"umap-2016-graus-et-al","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/01\/umap-2016-graus-et-al.pdf","id":359792,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"David Graus","user_id":0,"rest_url":false},{"type":"edited_text","value":"Paul N. 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