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    This study introduces a novel Hidden Markov Model (HMM) to analyze mobile app popularity, enhancing app services. The approach effectively models app trends for better recommendations and fraud detection.

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    Area of Science:

    • Computer Science
    • Data Science
    • Mobile Computing

    Background:

    • Mobile app popularity metrics (charts, ratings, reviews) offer insights into user experience and adoption.
    • Existing research recognizes the value of popularity data but lacks a unified approach for its application in mobile app services.

    Purpose of the Study:

    • To develop a robust sequential modeling approach for mobile app popularity data.
    • To enhance the utility of app popularity information for a range of mobile app services.

    Main Methods:

    • Proposed a popularity-based Hidden Markov Model (PHMM) to model sequences of heterogeneous app popularity observations.
    • Introduced a bipartite clustering method for efficient pre-clustering of popularity data, aiding PHMM parameter learning.
    • Demonstrated the general applicability of the PHMM across various app services.

    Main Results:

    • The PHMM effectively models sequential app popularity data.
    • The bipartite pre-clustering method improves the efficiency of PHMM parameter estimation.
    • Validated the approach on real-world datasets from the Apple App Store, confirming its effectiveness and efficiency.

    Conclusions:

    • The proposed PHMM offers a generalized and effective framework for modeling mobile app popularity.
    • This approach can significantly improve diverse mobile app services, including recommendations and fraud detection.
    • The study highlights the potential of advanced statistical modeling for leveraging app store data.