Popularity Modeling for Mobile Apps: A Sequential Approach

Hengshu Zhu, Chuanren Liu, Yong Ge, Hui Xiong, Enhong Chen

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

The popularity information in App stores, such as chart rankings, user ratings, and user reviews, provides an unprecedented opportunity to understand user experiences with mobile Apps, learn the process of adoption of mobile Apps, and thus enables better mobile App services. While the importance of popularity information is well recognized in the literature, the use of the popularity information for mobile App services is still fragmented and under-explored. To this end, in this paper, we propose a sequential approach based on hidden Markov model (HMM) for modeling the popularity information of mobile Apps toward mobile App services. Specifically, we first propose a popularity based HMM (PHMM) to model the sequences of the heterogeneous popularity observations of mobile Apps. Then, we introduce a bipartite based method to precluster the popularity observations. This can help to learn the parameters and initial values of the PHMM efficiently. Furthermore, we demonstrate that the PHMM is a general model and can be applicable for various mobile App services, such as trend based App recommendation, rating and review spam detection, and ranking fraud detection. Finally, we validate our approach on two real-world data sets collected from the Apple Appstore. Experimental results clearly validate both the effectiveness and efficiency of the proposed popularity modeling approach.

Original languageEnglish (US)
Article number6891300
Pages (from-to)1303-1314
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume45
Issue number7
DOIs
StatePublished - Jul 1 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • App recommendation
  • hidden Markov models (HMMs)
  • mobile Apps
  • popularity modeling

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