Abstract
As mobile apps continue to grow, app stores, as the main channel for users to download apps, are becoming increasingly important for developers and platforms. Understanding users 'download behavior and accurately predicting users' preferences and needs can effectively improve the effect of the recommendation system in the application mall, improve user experience and improve the download conversion rate. However, traditional rule-based recommender systems often face the problems of data sparsity and model complexity. Therefore, it is an urgent and valuable topic to analyze user download behavior combined with machine learning technology and to provide personalized recommendations and services. This study uses the download behavior information of Google Play Store users in 2018, and use three classic machine algorithms (linear regression, random forest and SVM) to model and predict the software rating, dig deep into various factors affecting the rating, and gain deep insight into users' preferences and behavior patterns. This will provide more accurate recommendation results for the application mall, improve the application quality and popularity, and improve the user satisfaction and loyalty, and provide an important reference for optimizing the recommendation system and personalized service of the application mall.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Advances in Economics, Management and Political Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
