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Withdrawn paper details AI framework for predicting app ratings

By PulseAugur Editorial · [1 sources] ·

A research paper proposes a new lightweight framework for predicting mobile app ratings by fusing visual and textual data. The model combines MobileNetV3 for UI features and DistilBERT for semantic information, achieving strong performance metrics including a mean absolute error of 0.1060. This approach aims to provide developers with better insights into user satisfaction and enable efficient deployment on edge devices. However, the paper has since been withdrawn by its author, Azrin Sultana. AI

IMPACT This framework could improve app development by providing better user satisfaction predictions, though its withdrawal limits immediate impact.

RANK_REASON The item is a withdrawn academic paper detailing a novel framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Withdrawn paper details AI framework for predicting app ratings

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The item is a withdrawn academic paper detailing a novel framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Azrin Sultana, Firoz Ahmed ·

    A Lightweight Vision-Language Fusion Framework for Predicting App Ratings from User Interfaces and Metadata

    arXiv:2602.20531v2 Announce Type: replace Abstract: App ratings are among the most significant indicators of the quality, usability, and overall user satisfaction of mobile applications. However, existing app rating prediction models are largely limited to textual data or user in…

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