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To address this challenge, we aim at developing an intelligent mobile system that automatically infers moments in which users are open to engage with suggested content. To inform the development of such a system, we carried out a field study with 337 mobile phone users. For 4 weeks, participants ran a study application on their primary phones. They were tasked to frequently report their current mood via a notification-administered experience-sampling questionnaire. In this study, however, we analyze whether they voluntarily engaged with content that we offered at the bottom of that questionnaire. In addition, the study app logged a wide range of data related to their phone use. Based on 120 Million phone-use events and 78,930 questionnaire notifications, we build a machine-learning model that before delivering a notification predicts whether a participant will click on the notification and subsequently engage with the offered content. When compared to a na\u00efve baseline, which emulates current non-intelligent engagement strategies, our model achieves 66.6% higher success rate in its predictions. If the model also considers the user's past behavior, predictions improve 5-fold over the baseline. Based on these findings, we discuss the implications for building an intelligent service that identifies opportune moments for proactive user engagement, while, at the same time, reduces the number of undesirable interruptions.<\/jats:p>","DOI":"10.1145\/3130956","type":"journal-article","created":{"date-parts":[[2017,9,11]],"date-time":"2017-09-11T12:12:26Z","timestamp":1505131946000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":118,"title":["Beyond Interruptibility"],"prefix":"10.1145","volume":"1","author":[{"given":"Martin","family":"Pielot","sequence":"first","affiliation":[{"name":"Telef\u00f3nica Research"}]},{"given":"Bruno","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Universidade Nova de Lisboa"}]},{"given":"Kleomenis","family":"Katevas","sequence":"additional","affiliation":[{"name":"Queen Mary University of London"}]},{"given":"Joan","family":"Serr\u00e0","sequence":"additional","affiliation":[{"name":"Telefonica Research"}]},{"given":"Aleksandar","family":"Matic","sequence":"additional","affiliation":[{"name":"Telefonica Alpha"}]},{"given":"Nuria","family":"Oliver","sequence":"additional","affiliation":[{"name":"Telefonica Research, now at Data-Pop Alliance and Vodafone Research"}]}],"member":"320","published-online":{"date-parts":[[2017,9,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971712"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/985692.985727"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2971451"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1124772.1124881"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_1_6_1","unstructured":"L. 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