TY - JOUR
T1 - Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data
T2 - A Review
AU - Guo, Bin
AU - Ouyang, Yi
AU - Guo, Tong
AU - Cao, Longbing
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented.
AB - The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented.
KW - App marketing
KW - app recommendation
KW - mobile crowdsourcing
KW - popularity prediction
KW - usage pattern mining
KW - user profiling
UR - http://www.scopus.com/inward/record.url?scp=85067209934&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2918325
DO - 10.1109/ACCESS.2019.2918325
M3 - 文献综述
AN - SCOPUS:85067209934
SN - 2169-3536
VL - 7
SP - 68557
EP - 68571
JO - IEEE Access
JF - IEEE Access
M1 - 8720158
ER -