{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:10:12Z","timestamp":1780441812343,"version":"3.54.1"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T00:00:00Z","timestamp":1603929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["L182038"],"award-info":[{"award-number":["L182038"]}]},{"name":"Tsinghua University-Tencent Joint Laboratory for Internet Innovation Technology"},{"name":"National Key Research and Development Program of China","award":["SQ2018YFB180012"],"award-info":[{"award-number":["SQ2018YFB180012"]}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61971267, 61972223, 61861136003, and 61621091"],"award-info":[{"award-number":["61971267, 61972223, 61861136003, and 61621091"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100017582","name":"Beijing National Research Center for Information Science and Technology","doi-asserted-by":"crossref","award":["20031887521"],"award-info":[{"award-number":["20031887521"]}],"id":[{"id":"10.13039\/501100017582","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>\n            Smartphone mobile application (App) usage prediction, i.e., which Apps will be used next, is beneficial for user experience improvement. Through an in-depth analysis on a real-world dataset, we find that App usage is highly spatio-temporally correlated and personalized. Given the ability to model complex spatio-temporal contexts, we aim to apply deep learning to achieve high prediction accuracy. However, the personalization yields a problem: training one network for each individual suffers from data scarcity, yet training one deep neural network for all users often fails to uncover user preference. In this article, we propose a novel App usage prediction framework, named\n            <jats:italic>DeepApp<\/jats:italic>\n            , to achieve context-aware prediction via multi-task learning. To tackle the challenge of data scarcity, we train one general network for multiple users to share common patterns. To better utilize the spatio-temporal contexts, we supplement a location prediction task in the multi-task learning framework to learn spatio-temporal relations. As for the personalization, we add a user identification task to capture user preference. We evaluate DeepApp on the large-scale dataset by extensive experiments. Results demonstrate that DeepApp outperforms the start-of-the-art baseline by 6.44%.\n          <\/jats:p>","DOI":"10.1145\/3408325","type":"journal-article","created":{"date-parts":[[2020,10,29]],"date-time":"2020-10-29T22:09:02Z","timestamp":1604009342000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["DeepApp"],"prefix":"10.1145","volume":"11","author":[{"given":"Tong","family":"Xia","sequence":"first","affiliation":[{"name":"Tsinghua University, Haidian District, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Haidian District, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Feng","sequence":"additional","affiliation":[{"name":"Tsinghua University, Haidian District, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Depeng","family":"Jin","sequence":"additional","affiliation":[{"name":"Tsinghua University, Haidian District, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Meituan-Dianping Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengliang","family":"Luo","sequence":"additional","affiliation":[{"name":"Meituan-Dianping Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingmin","family":"Liao","sequence":"additional","affiliation":[{"name":"Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,10,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622248.1622254"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2017.01.007"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06605-9_16"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3314391"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939875"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186058"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2181196.2181199"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370442"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7482--7491","author":"Kendall Alex","year":"2018","unstructured":"Alex Kendall , Yarin Gal , and Roberto Cipolla . 2018 . 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Which app will you use next? Collaborative filtering with interactional context . In Proceedings of the 7th ACM Conference on Recommender Systems. ACM , New York, NY, 201--208. Nagarajan Natarajan, Donghyuk Shin, and Inderjit S. Dhillon. 2013. Which app will you use next? Collaborative filtering with interactional context. In Proceedings of the 7th ACM Conference on Recommender Systems. ACM, New York, NY, 201--208."},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM","author":"Parate Abhinav","unstructured":"Abhinav Parate , Matthias B\u00f6hmer , David Chu , Deepak Ganesan , and Benjamin M. Marlin . 2013. Practical prediction and prefetch for faster access to applications on mobile phones . In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM , New York, NY, 275--284. Abhinav Parate, Matthias B\u00f6hmer, David Chu, Deepak Ganesan, and Benjamin M. Marlin. 2013. Practical prediction and prefetch for faster access to applications on mobile phones. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, New York, NY, 275--284."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3356250.3360038"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330828"},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM","author":"Shin Choonsung","unstructured":"Choonsung Shin , Jin-Hyuk Hong , and Anind K. Dey . 2012. Understanding and prediction of mobile application usage for smart phones . In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM , New York, NY, 173--182. Choonsung Shin, Jin-Hyuk Hong, and Anind K. Dey. 2012. Understanding and prediction of mobile application usage for smart phones. 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