Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 59 , 05 January 2024
Open
Access | Article
Download Behavior Analysis Based on Google Play Store Data
* Author to whom correspondence should be addressed.
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.
Keywords
Google Play Store, behavioral analysis, random forest, linear regression, SVM
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-209-1
- ISBN (Online)
- 978-1-83558-210-7
- Published Date
- 05 January 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/59/20231091
- Copyright
- 05 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited