| Through a gender lens: Learning usage patterns of emojis from large-scale android users Z Chen, X Lu, W Ai, H Li, Q Mei, X Liu Proceedings of the 2018 world wide web conference, 763-772, 2018 | 158 | 2018 |
| Characterizing smartphone usage patterns from millions of android users H Li, X Lu, X Liu, T Xie, K Bian, FX Lin, Q Mei, F Feng Proceedings of the 2015 Internet Measurement Conference, 459-472, 2015 | 145 | 2015 |
| PRADA: Prioritizing android devices for apps by mining large-scale usage data X Lu, X Liu, H Li, T Xie, Q Mei, D Hao, G Huang, F Feng Proceedings of the 38th International Conference on Software Engineering, 3-13, 2016 | 65 | 2016 |
| Understanding diverse usage patterns from large-scale appstore-service profiles X Liu, H Li, X Lu, T Xie, Q Mei, F Feng, H Mei IEEE Transactions on Software Engineering 44 (4), 384-411, 2017 | 64 | 2017 |
| Voting with their feet: Inferring user preferences from app management activities H Li, W Ai, X Liu, J Tang, G Huang, F Feng, Q Mei Proceedings of the 25th international conference on world wide web, 1351-1362, 2016 | 40 | 2016 |
| Deriving user preferences of mobile apps from their management activities X Liu, W Ai, H Li, J Tang, G Huang, F Feng, Q Mei ACM Transactions on Information Systems (TOIS) 35 (4), 1-32, 2017 | 39 | 2017 |
| Predicting smartphone battery life based on comprehensive and real-time usage data H Li, X Liu, Q Mei arXiv preprint arXiv:1801.04069, 2018 | 33 | 2018 |
| Prado: Predicting app adoption by learning the correlation between developer-controllable properties and user behaviors X Lu, Z Chen, X Liu, H Li, T Xie, Q Mei Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2017 | 23 | 2017 |
| Mining device-specific apps usage patterns from large-scale android users H Li, X Lu arXiv preprint arXiv:1707.09252, 2017 | 12 | 2017 |
| A descriptive analysis of a large-scale collection of app management activities H Li, X Liu, W Ai, Q Mei, F Feng Proceedings of the 24th International Conference on World Wide Web, 61-62, 2015 | 9 | 2015 |
| Systematic analysis of fine-grained mobility prediction with on-device contextual data H Li, F Lin, X Lu, C Xu, G Huang, J Zhang, Q Mei, X Liu IEEE Transactions on Mobile Computing 21 (3), 1096-1109, 2020 | 8 | 2020 |
| Security analytics for mobile apps: achievements and challenges W Yang, XS Xiao, D Li, H Li, H Wang, Y Guo, T Xie Journal of Cyber Security 1 (2), 1-14, 2016 | 8 | 2016 |
| Mining usage data from large-scale android users: challenges and opportunities X Lu, X Liu, H Li, T Xie, Q Mei, D Hao, G Huang, F Feng Proceedings of the International Conference on Mobile Software Engineering …, 2016 | 4 | 2016 |
| Mining behavioral patterns from millions of android users X Liu, H Li, X Lu, T Xie, Q Mei, H Mei, F Feng arXiv preprint arXiv:1702.05060, 2017 | 1 | 2017 |
| Adoption of Recurrent Innovations: A Large-Scale Case Study on Mobile App Updates F Lin, X Lu, W Ai, H Li, Y Ma, Y Yang, H Deng, Q Wang, Q Mei, X Liu ACM Transactions on the Web 18 (1), 1-26, 2023 | | 2023 |
| Method and system for determining quality of application based on user behaviors of application management X Liu, G Huang, H Mei, H Li, X Lu US Patent 10,997,637, 2021 | | 2021 |
| A Systematic Analysis of Fine-Grained Human Mobility Prediction with On-Device Contextual Data H Li arXiv preprint arXiv:1901.10167, 2019 | | 2019 |
| Method of selecting mobile device models for application development on basis of user operational profiles X Liu, G Huang, H Mei, X Lu, H Li US Patent App. 15/746,450, 2018 | | 2018 |
| Mining Device-Specific Apps Usage Patterns from Appstore Big Data H Li, X Liu, H Mei, Q Mei Big Data: 6th CCF Conference, Big Data 2018, Xi'an, China, October 11-13 …, 2018 | | 2018 |
| IEEE/ACM 38th IEEE X Lu, X Liu, H Li, T Xie, Q Mei, D Hao, G Huang, F Feng | | |
Xuanzhe LiuBoya Distinguished Professor of Computer Science, Peking University, ACM Distinguished ScientistBestätigte E-Mail-Adresse bei pku.edu.cn
