TY - GEN
T1 - CrowdPop
T2 - 10th Asia-Pacific Symposium on Internetware, Internetware 2018
AU - Zhang, Yixuan
AU - Guo, Bin
AU - Ouyang, Yi
AU - Guo, Tong
AU - Wang, Zhu
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/9/16
Y1 - 2018/9/16
N2 - The popularity prediction of mobile apps provides substantial value to a broad range of applications, ranging from app development to targeted advertising. However, most previous studies do this work by establishing regression models for impact factors, or using clustering and classification algorithms. It does not fully investigate the process of popularity evolution and the reasons behind it. In this paper, we discuss and analyze the potential predictors, especially the impact of early evolutionary patterns on future popularity. To this end, we first explore six basic evolutionary patterns and six impact factors that are closely related to app popularity. After detailed analysis, we present CrowdPop, a popularity prediction model based on the Random Forest algorithm, to quantify patterns and factors as predictors of CrowdPop. The experiment results with a real-world dataset of 126 apps indicate that, compared with baseline methods, our CrowdPop performs better in mobile app popularity prediction.
AB - The popularity prediction of mobile apps provides substantial value to a broad range of applications, ranging from app development to targeted advertising. However, most previous studies do this work by establishing regression models for impact factors, or using clustering and classification algorithms. It does not fully investigate the process of popularity evolution and the reasons behind it. In this paper, we discuss and analyze the potential predictors, especially the impact of early evolutionary patterns on future popularity. To this end, we first explore six basic evolutionary patterns and six impact factors that are closely related to app popularity. After detailed analysis, we present CrowdPop, a popularity prediction model based on the Random Forest algorithm, to quantify patterns and factors as predictors of CrowdPop. The experiment results with a real-world dataset of 126 apps indicate that, compared with baseline methods, our CrowdPop performs better in mobile app popularity prediction.
KW - App evolutionary pattern mining
KW - App popularity prediction
KW - Crowd-contributed data
KW - Mobile app analysis
UR - http://www.scopus.com/inward/record.url?scp=85056703223&partnerID=8YFLogxK
U2 - 10.1145/3275219.3275235
DO - 10.1145/3275219.3275235
M3 - 会议稿件
AN - SCOPUS:85056703223
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 10th Asia-Pacific Symposium on Internetware, Internetware 2018
PB - Association for Computing Machinery
Y2 - 16 September 2018 through 16 September 2018
ER -