{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:25:24Z","timestamp":1760235924126,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T00:00:00Z","timestamp":1633564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new users or newly installed Apps. There are some existing works related to mobile App start-up prediction using multi-user data, which require the integration of multi-party data. In this case, a typical solution is distributed learning of centralized computing. However, this solution can easily lead to the leakage of user privacy data. In this paper, we propose a mobile App start-up prediction method based on federated learning and attributed heterogeneous network embedding, which alleviates the cold start problem of new users or new Apps while guaranteeing users\u2019 privacy.<\/jats:p>","DOI":"10.3390\/fi13100256","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T08:41:49Z","timestamp":1633682509000},"page":"256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Mobile App Start-Up Prediction Based on Federated Learning and Attributed Heterogeneous Network Embedding"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9960-5824","authenticated-orcid":false,"given":"Shaoyong","family":"Li","sequence":"first","affiliation":[{"name":"College of Mathematics and Computer Science, Changsha University, Changsha 410083, China"},{"name":"College of Computer Science, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Tsinghua University, Beijing 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoya","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Changsha University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyun","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pmcj.2017.01.007","article-title":"Mining smartphone data for app usage prediction and recommendations: A survey","volume":"37","author":"Ca","year":"2017","journal-title":"Pervasive Mob. 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