Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review

Bin Guo, Yi Ouyang, Tong Guo, Longbing Cao, Zhiwen Yu

Research output: Contribution to journalReview articlepeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish
Article number8720158
Pages (from-to)68557-68571
Number of pages15
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019

Keywords

  • App marketing
  • app recommendation
  • mobile crowdsourcing
  • popularity prediction
  • usage pattern mining
  • user profiling

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