arbiter
JournalsPricingAccount

Prediction of success factors for mobile application using machine learning technique

Jyoti Deone et al.

International Journal of Data Analysis Techniques and Strategies2025https://doi.org/10.1504/ijdats.2025.144965article
AJG 1
Weight
0.37

What the paper says

The remarkable boom in the mobile market has attracted many developers to build mobile apps. However, the majority of developers are suffering to generate earnings. For those developers, knowing the characteristics of successful apps may be very vital. We propose an approach which examines the categories of apps by two factors. First, the correlation is measured between app features and secondly, concepts are extracted from apps to understand the common theme present in them. For this, we selected 3000 applications available in the Google Play Store. The observations specify that there may be a strong correlation among purchaser rating and the quantity of app downloads, though there may be no correlation between rate and downloads, nor among charge and rating. Moreover, we find standards unique to excessive rated apps and low rated apps. The correlation along with the concepts proves useful for application developers to understand the market trend and customer demand more easily than earlier approaches.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1504/ijdats.2025.144965

Or copy a formatted citation

@article{jyoti2025,
  title        = {{Prediction of success factors for mobile application using machine learning technique}},
  author       = {Jyoti Deone et al.},
  journal      = {International Journal of Data Analysis Techniques and Strategies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijdats.2025.144965},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Prediction of success factors for mobile application using machine learning technique

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.