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Understanding Diverse Smarpthone Usage Patterns from Large-Scale Appstore-Service Profiles.

, , , , , , and . CoRR, (2017)

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Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users., , , , , and . WWW, page 763-772. ACM, (2018)A Descriptive Analysis of a Large-Scale Collection of App Management Activities., , , , and . WWW (Companion Volume), page 61-62. ACM, (2015)Understanding Diverse Smarpthone Usage Patterns from Large-Scale Appstore-Service Profiles., , , , , , and . CoRR, (2017)Characterizing Smartphone Usage Patterns from Millions of Android Users., , , , , , , and . Internet Measurement Conference, page 459-472. ACM, (2015)PRADO: Predicting App Adoption by Learning the Correlation between Developer-Controllable Properties and User Behaviors., , , , , and . IMWUT, 1 (3): 79:1-79:30 (2017)Mining usage data from large-scale Android users: challenges and opportunities., , , , , , , and . MOBILESoft, page 301-302. ACM, (2016)PRADA: prioritizing android devices for apps by mining large-scale usage data., , , , , , , and . ICSE, page 3-13. ACM, (2016)Mining Device-Specific Apps Usage Patterns from Large-Scale Android Users., and . CoRR, (2017)Deriving User Preferences of Mobile Apps from Their Management Activities., , , , , , and . ACM Trans. Inf. Syst., 35 (4): 39:1-39:32 (2017)Systematic Analysis of Fine-Grained Mobility Prediction With On-Device Contextual Data., , , , , , , and . IEEE Trans. Mob. Comput., 21 (3): 1096-1109 (2022)